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<h1><a href="aiplatform_v1beta1.html">Vertex AI API</a> . <a href="aiplatform_v1beta1.projects.html">projects</a> . <a href="aiplatform_v1beta1.projects.locations.html">locations</a> . <a href="aiplatform_v1beta1.projects.locations.modelMonitors.html">modelMonitors</a></h1>
<h2>Instance Methods</h2>
<p class="toc_element">
  <code><a href="aiplatform_v1beta1.projects.locations.modelMonitors.modelMonitoringJobs.html">modelMonitoringJobs()</a></code>
</p>
<p class="firstline">Returns the modelMonitoringJobs Resource.</p>

<p class="toc_element">
  <code><a href="aiplatform_v1beta1.projects.locations.modelMonitors.operations.html">operations()</a></code>
</p>
<p class="firstline">Returns the operations Resource.</p>

<p class="toc_element">
  <code><a href="#close">close()</a></code></p>
<p class="firstline">Close httplib2 connections.</p>
<p class="toc_element">
  <code><a href="#create">create(parent, body=None, modelMonitorId=None, x__xgafv=None)</a></code></p>
<p class="firstline">Creates a ModelMonitor.</p>
<p class="toc_element">
  <code><a href="#delete">delete(name, force=None, x__xgafv=None)</a></code></p>
<p class="firstline">Deletes a ModelMonitor.</p>
<p class="toc_element">
  <code><a href="#get">get(name, x__xgafv=None)</a></code></p>
<p class="firstline">Gets a ModelMonitor.</p>
<p class="toc_element">
  <code><a href="#list">list(parent, filter=None, pageSize=None, pageToken=None, readMask=None, x__xgafv=None)</a></code></p>
<p class="firstline">Lists ModelMonitors in a Location.</p>
<p class="toc_element">
  <code><a href="#list_next">list_next()</a></code></p>
<p class="firstline">Retrieves the next page of results.</p>
<p class="toc_element">
  <code><a href="#patch">patch(name, body=None, updateMask=None, x__xgafv=None)</a></code></p>
<p class="firstline">Updates a ModelMonitor.</p>
<p class="toc_element">
  <code><a href="#searchModelMonitoringAlerts">searchModelMonitoringAlerts(modelMonitor, body=None, x__xgafv=None)</a></code></p>
<p class="firstline">Returns the Model Monitoring alerts.</p>
<p class="toc_element">
  <code><a href="#searchModelMonitoringAlerts_next">searchModelMonitoringAlerts_next()</a></code></p>
<p class="firstline">Retrieves the next page of results.</p>
<p class="toc_element">
  <code><a href="#searchModelMonitoringStats">searchModelMonitoringStats(modelMonitor, body=None, x__xgafv=None)</a></code></p>
<p class="firstline">Searches Model Monitoring Stats generated within a given time window.</p>
<p class="toc_element">
  <code><a href="#searchModelMonitoringStats_next">searchModelMonitoringStats_next()</a></code></p>
<p class="firstline">Retrieves the next page of results.</p>
<h3>Method Details</h3>
<div class="method">
    <code class="details" id="close">close()</code>
  <pre>Close httplib2 connections.</pre>
</div>

<div class="method">
    <code class="details" id="create">create(parent, body=None, modelMonitorId=None, x__xgafv=None)</code>
  <pre>Creates a ModelMonitor.

Args:
  parent: string, Required. The resource name of the Location to create the ModelMonitor in. Format: `projects/{project}/locations/{location}` (required)
  body: object, The request body.
    The object takes the form of:

{ # Vertex AI Model Monitoring Service serves as a central hub for the analysis and visualization of data quality and performance related to models. ModelMonitor stands as a top level resource for overseeing your model monitoring tasks.
  &quot;createTime&quot;: &quot;A String&quot;, # Output only. Timestamp when this ModelMonitor was created.
  &quot;displayName&quot;: &quot;A String&quot;, # The display name of the ModelMonitor. The name can be up to 128 characters long and can consist of any UTF-8.
  &quot;encryptionSpec&quot;: { # Represents a customer-managed encryption key spec that can be applied to a top-level resource. # Customer-managed encryption key spec for a ModelMonitor. If set, this ModelMonitor and all sub-resources of this ModelMonitor will be secured by this key.
    &quot;kmsKeyName&quot;: &quot;A String&quot;, # Required. The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: `projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key`. The key needs to be in the same region as where the compute resource is created.
  },
  &quot;explanationSpec&quot;: { # Specification of Model explanation. # Optional model explanation spec. It is used for feature attribution monitoring.
    &quot;metadata&quot;: { # Metadata describing the Model&#x27;s input and output for explanation. # Optional. Metadata describing the Model&#x27;s input and output for explanation.
      &quot;featureAttributionsSchemaUri&quot;: &quot;A String&quot;, # Points to a YAML file stored on Google Cloud Storage describing the format of the feature attributions. The schema is defined as an OpenAPI 3.0.2 [Schema Object](https://github.com/OAI/OpenAPI-Specification/blob/main/versions/3.0.2.md#schemaObject). AutoML tabular Models always have this field populated by Vertex AI. Note: The URI given on output may be different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
      &quot;inputs&quot;: { # Required. Map from feature names to feature input metadata. Keys are the name of the features. Values are the specification of the feature. An empty InputMetadata is valid. It describes a text feature which has the name specified as the key in ExplanationMetadata.inputs. The baseline of the empty feature is chosen by Vertex AI. For Vertex AI-provided Tensorflow images, the key can be any friendly name of the feature. Once specified, featureAttributions are keyed by this key (if not grouped with another feature). For custom images, the key must match with the key in instance.
        &quot;a_key&quot;: { # Metadata of the input of a feature. Fields other than InputMetadata.input_baselines are applicable only for Models that are using Vertex AI-provided images for Tensorflow.
          &quot;denseShapeTensorName&quot;: &quot;A String&quot;, # Specifies the shape of the values of the input if the input is a sparse representation. Refer to Tensorflow documentation for more details: https://www.tensorflow.org/api_docs/python/tf/sparse/SparseTensor.
          &quot;encodedBaselines&quot;: [ # A list of baselines for the encoded tensor. The shape of each baseline should match the shape of the encoded tensor. If a scalar is provided, Vertex AI broadcasts to the same shape as the encoded tensor.
            &quot;&quot;,
          ],
          &quot;encodedTensorName&quot;: &quot;A String&quot;, # Encoded tensor is a transformation of the input tensor. Must be provided if choosing Integrated Gradients attribution or XRAI attribution and the input tensor is not differentiable. An encoded tensor is generated if the input tensor is encoded by a lookup table.
          &quot;encoding&quot;: &quot;A String&quot;, # Defines how the feature is encoded into the input tensor. Defaults to IDENTITY.
          &quot;featureValueDomain&quot;: { # Domain details of the input feature value. Provides numeric information about the feature, such as its range (min, max). If the feature has been pre-processed, for example with z-scoring, then it provides information about how to recover the original feature. For example, if the input feature is an image and it has been pre-processed to obtain 0-mean and stddev = 1 values, then original_mean, and original_stddev refer to the mean and stddev of the original feature (e.g. image tensor) from which input feature (with mean = 0 and stddev = 1) was obtained. # The domain details of the input feature value. Like min/max, original mean or standard deviation if normalized.
            &quot;maxValue&quot;: 3.14, # The maximum permissible value for this feature.
            &quot;minValue&quot;: 3.14, # The minimum permissible value for this feature.
            &quot;originalMean&quot;: 3.14, # If this input feature has been normalized to a mean value of 0, the original_mean specifies the mean value of the domain prior to normalization.
            &quot;originalStddev&quot;: 3.14, # If this input feature has been normalized to a standard deviation of 1.0, the original_stddev specifies the standard deviation of the domain prior to normalization.
          },
          &quot;groupName&quot;: &quot;A String&quot;, # Name of the group that the input belongs to. Features with the same group name will be treated as one feature when computing attributions. Features grouped together can have different shapes in value. If provided, there will be one single attribution generated in Attribution.feature_attributions, keyed by the group name.
          &quot;indexFeatureMapping&quot;: [ # A list of feature names for each index in the input tensor. Required when the input InputMetadata.encoding is BAG_OF_FEATURES, BAG_OF_FEATURES_SPARSE, INDICATOR.
            &quot;A String&quot;,
          ],
          &quot;indicesTensorName&quot;: &quot;A String&quot;, # Specifies the index of the values of the input tensor. Required when the input tensor is a sparse representation. Refer to Tensorflow documentation for more details: https://www.tensorflow.org/api_docs/python/tf/sparse/SparseTensor.
          &quot;inputBaselines&quot;: [ # Baseline inputs for this feature. If no baseline is specified, Vertex AI chooses the baseline for this feature. If multiple baselines are specified, Vertex AI returns the average attributions across them in Attribution.feature_attributions. For Vertex AI-provided Tensorflow images (both 1.x and 2.x), the shape of each baseline must match the shape of the input tensor. If a scalar is provided, we broadcast to the same shape as the input tensor. For custom images, the element of the baselines must be in the same format as the feature&#x27;s input in the instance[]. The schema of any single instance may be specified via Endpoint&#x27;s DeployedModels&#x27; Model&#x27;s PredictSchemata&#x27;s instance_schema_uri.
            &quot;&quot;,
          ],
          &quot;inputTensorName&quot;: &quot;A String&quot;, # Name of the input tensor for this feature. Required and is only applicable to Vertex AI-provided images for Tensorflow.
          &quot;modality&quot;: &quot;A String&quot;, # Modality of the feature. Valid values are: numeric, image. Defaults to numeric.
          &quot;visualization&quot;: { # Visualization configurations for image explanation. # Visualization configurations for image explanation.
            &quot;clipPercentLowerbound&quot;: 3.14, # Excludes attributions below the specified percentile, from the highlighted areas. Defaults to 62.
            &quot;clipPercentUpperbound&quot;: 3.14, # Excludes attributions above the specified percentile from the highlighted areas. Using the clip_percent_upperbound and clip_percent_lowerbound together can be useful for filtering out noise and making it easier to see areas of strong attribution. Defaults to 99.9.
            &quot;colorMap&quot;: &quot;A String&quot;, # The color scheme used for the highlighted areas. Defaults to PINK_GREEN for Integrated Gradients attribution, which shows positive attributions in green and negative in pink. Defaults to VIRIDIS for XRAI attribution, which highlights the most influential regions in yellow and the least influential in blue.
            &quot;overlayType&quot;: &quot;A String&quot;, # How the original image is displayed in the visualization. Adjusting the overlay can help increase visual clarity if the original image makes it difficult to view the visualization. Defaults to NONE.
            &quot;polarity&quot;: &quot;A String&quot;, # Whether to only highlight pixels with positive contributions, negative or both. Defaults to POSITIVE.
            &quot;type&quot;: &quot;A String&quot;, # Type of the image visualization. Only applicable to Integrated Gradients attribution. OUTLINES shows regions of attribution, while PIXELS shows per-pixel attribution. Defaults to OUTLINES.
          },
        },
      },
      &quot;latentSpaceSource&quot;: &quot;A String&quot;, # Name of the source to generate embeddings for example based explanations.
      &quot;outputs&quot;: { # Required. Map from output names to output metadata. For Vertex AI-provided Tensorflow images, keys can be any user defined string that consists of any UTF-8 characters. For custom images, keys are the name of the output field in the prediction to be explained. Currently only one key is allowed.
        &quot;a_key&quot;: { # Metadata of the prediction output to be explained.
          &quot;displayNameMappingKey&quot;: &quot;A String&quot;, # Specify a field name in the prediction to look for the display name. Use this if the prediction contains the display names for the outputs. The display names in the prediction must have the same shape of the outputs, so that it can be located by Attribution.output_index for a specific output.
          &quot;indexDisplayNameMapping&quot;: &quot;&quot;, # Static mapping between the index and display name. Use this if the outputs are a deterministic n-dimensional array, e.g. a list of scores of all the classes in a pre-defined order for a multi-classification Model. It&#x27;s not feasible if the outputs are non-deterministic, e.g. the Model produces top-k classes or sort the outputs by their values. The shape of the value must be an n-dimensional array of strings. The number of dimensions must match that of the outputs to be explained. The Attribution.output_display_name is populated by locating in the mapping with Attribution.output_index.
          &quot;outputTensorName&quot;: &quot;A String&quot;, # Name of the output tensor. Required and is only applicable to Vertex AI provided images for Tensorflow.
        },
      },
    },
    &quot;parameters&quot;: { # Parameters to configure explaining for Model&#x27;s predictions. # Required. Parameters that configure explaining of the Model&#x27;s predictions.
      &quot;examples&quot;: { # Example-based explainability that returns the nearest neighbors from the provided dataset. # Example-based explanations that returns the nearest neighbors from the provided dataset.
        &quot;exampleGcsSource&quot;: { # The Cloud Storage input instances. # The Cloud Storage input instances.
          &quot;dataFormat&quot;: &quot;A String&quot;, # The format in which instances are given, if not specified, assume it&#x27;s JSONL format. Currently only JSONL format is supported.
          &quot;gcsSource&quot;: { # The Google Cloud Storage location for the input content. # The Cloud Storage location for the input instances.
            &quot;uris&quot;: [ # Required. Google Cloud Storage URI(-s) to the input file(s). May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/wildcards.
              &quot;A String&quot;,
            ],
          },
        },
        &quot;gcsSource&quot;: { # The Google Cloud Storage location for the input content. # The Cloud Storage locations that contain the instances to be indexed for approximate nearest neighbor search.
          &quot;uris&quot;: [ # Required. Google Cloud Storage URI(-s) to the input file(s). May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/wildcards.
            &quot;A String&quot;,
          ],
        },
        &quot;nearestNeighborSearchConfig&quot;: &quot;&quot;, # The full configuration for the generated index, the semantics are the same as metadata and should match [NearestNeighborSearchConfig](https://cloud.google.com/vertex-ai/docs/explainable-ai/configuring-explanations-example-based#nearest-neighbor-search-config).
        &quot;neighborCount&quot;: 42, # The number of neighbors to return when querying for examples.
        &quot;presets&quot;: { # Preset configuration for example-based explanations # Simplified preset configuration, which automatically sets configuration values based on the desired query speed-precision trade-off and modality.
          &quot;modality&quot;: &quot;A String&quot;, # The modality of the uploaded model, which automatically configures the distance measurement and feature normalization for the underlying example index and queries. If your model does not precisely fit one of these types, it is okay to choose the closest type.
          &quot;query&quot;: &quot;A String&quot;, # Preset option controlling parameters for speed-precision trade-off when querying for examples. If omitted, defaults to `PRECISE`.
        },
      },
      &quot;integratedGradientsAttribution&quot;: { # An attribution method that computes the Aumann-Shapley value taking advantage of the model&#x27;s fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1703.01365 # An attribution method that computes Aumann-Shapley values taking advantage of the model&#x27;s fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1703.01365
        &quot;blurBaselineConfig&quot;: { # Config for blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383 # Config for IG with blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383
          &quot;maxBlurSigma&quot;: 3.14, # The standard deviation of the blur kernel for the blurred baseline. The same blurring parameter is used for both the height and the width dimension. If not set, the method defaults to the zero (i.e. black for images) baseline.
        },
        &quot;smoothGradConfig&quot;: { # Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf # Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
          &quot;featureNoiseSigma&quot;: { # Noise sigma by features. Noise sigma represents the standard deviation of the gaussian kernel that will be used to add noise to interpolated inputs prior to computing gradients. # This is similar to noise_sigma, but provides additional flexibility. A separate noise sigma can be provided for each feature, which is useful if their distributions are different. No noise is added to features that are not set. If this field is unset, noise_sigma will be used for all features.
            &quot;noiseSigma&quot;: [ # Noise sigma per feature. No noise is added to features that are not set.
              { # Noise sigma for a single feature.
                &quot;name&quot;: &quot;A String&quot;, # The name of the input feature for which noise sigma is provided. The features are defined in explanation metadata inputs.
                &quot;sigma&quot;: 3.14, # This represents the standard deviation of the Gaussian kernel that will be used to add noise to the feature prior to computing gradients. Similar to noise_sigma but represents the noise added to the current feature. Defaults to 0.1.
              },
            ],
          },
          &quot;noiseSigma&quot;: 3.14, # This is a single float value and will be used to add noise to all the features. Use this field when all features are normalized to have the same distribution: scale to range [0, 1], [-1, 1] or z-scoring, where features are normalized to have 0-mean and 1-variance. Learn more about [normalization](https://developers.google.com/machine-learning/data-prep/transform/normalization). For best results the recommended value is about 10% - 20% of the standard deviation of the input feature. Refer to section 3.2 of the SmoothGrad paper: https://arxiv.org/pdf/1706.03825.pdf. Defaults to 0.1. If the distribution is different per feature, set feature_noise_sigma instead for each feature.
          &quot;noisySampleCount&quot;: 42, # The number of gradient samples to use for approximation. The higher this number, the more accurate the gradient is, but the runtime complexity increases by this factor as well. Valid range of its value is [1, 50]. Defaults to 3.
        },
        &quot;stepCount&quot;: 42, # Required. The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is within the desired error range. Valid range of its value is [1, 100], inclusively.
      },
      &quot;outputIndices&quot;: [ # If populated, only returns attributions that have output_index contained in output_indices. It must be an ndarray of integers, with the same shape of the output it&#x27;s explaining. If not populated, returns attributions for top_k indices of outputs. If neither top_k nor output_indices is populated, returns the argmax index of the outputs. Only applicable to Models that predict multiple outputs (e,g, multi-class Models that predict multiple classes).
        &quot;&quot;,
      ],
      &quot;sampledShapleyAttribution&quot;: { # An attribution method that approximates Shapley values for features that contribute to the label being predicted. A sampling strategy is used to approximate the value rather than considering all subsets of features. # An attribution method that approximates Shapley values for features that contribute to the label being predicted. A sampling strategy is used to approximate the value rather than considering all subsets of features. Refer to this paper for model details: https://arxiv.org/abs/1306.4265.
        &quot;pathCount&quot;: 42, # Required. The number of feature permutations to consider when approximating the Shapley values. Valid range of its value is [1, 50], inclusively.
      },
      &quot;topK&quot;: 42, # If populated, returns attributions for top K indices of outputs (defaults to 1). Only applies to Models that predicts more than one outputs (e,g, multi-class Models). When set to -1, returns explanations for all outputs.
      &quot;xraiAttribution&quot;: { # An explanation method that redistributes Integrated Gradients attributions to segmented regions, taking advantage of the model&#x27;s fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1906.02825 Supported only by image Models. # An attribution method that redistributes Integrated Gradients attribution to segmented regions, taking advantage of the model&#x27;s fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1906.02825 XRAI currently performs better on natural images, like a picture of a house or an animal. If the images are taken in artificial environments, like a lab or manufacturing line, or from diagnostic equipment, like x-rays or quality-control cameras, use Integrated Gradients instead.
        &quot;blurBaselineConfig&quot;: { # Config for blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383 # Config for XRAI with blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383
          &quot;maxBlurSigma&quot;: 3.14, # The standard deviation of the blur kernel for the blurred baseline. The same blurring parameter is used for both the height and the width dimension. If not set, the method defaults to the zero (i.e. black for images) baseline.
        },
        &quot;smoothGradConfig&quot;: { # Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf # Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
          &quot;featureNoiseSigma&quot;: { # Noise sigma by features. Noise sigma represents the standard deviation of the gaussian kernel that will be used to add noise to interpolated inputs prior to computing gradients. # This is similar to noise_sigma, but provides additional flexibility. A separate noise sigma can be provided for each feature, which is useful if their distributions are different. No noise is added to features that are not set. If this field is unset, noise_sigma will be used for all features.
            &quot;noiseSigma&quot;: [ # Noise sigma per feature. No noise is added to features that are not set.
              { # Noise sigma for a single feature.
                &quot;name&quot;: &quot;A String&quot;, # The name of the input feature for which noise sigma is provided. The features are defined in explanation metadata inputs.
                &quot;sigma&quot;: 3.14, # This represents the standard deviation of the Gaussian kernel that will be used to add noise to the feature prior to computing gradients. Similar to noise_sigma but represents the noise added to the current feature. Defaults to 0.1.
              },
            ],
          },
          &quot;noiseSigma&quot;: 3.14, # This is a single float value and will be used to add noise to all the features. Use this field when all features are normalized to have the same distribution: scale to range [0, 1], [-1, 1] or z-scoring, where features are normalized to have 0-mean and 1-variance. Learn more about [normalization](https://developers.google.com/machine-learning/data-prep/transform/normalization). For best results the recommended value is about 10% - 20% of the standard deviation of the input feature. Refer to section 3.2 of the SmoothGrad paper: https://arxiv.org/pdf/1706.03825.pdf. Defaults to 0.1. If the distribution is different per feature, set feature_noise_sigma instead for each feature.
          &quot;noisySampleCount&quot;: 42, # The number of gradient samples to use for approximation. The higher this number, the more accurate the gradient is, but the runtime complexity increases by this factor as well. Valid range of its value is [1, 50]. Defaults to 3.
        },
        &quot;stepCount&quot;: 42, # Required. The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is met within the desired error range. Valid range of its value is [1, 100], inclusively.
      },
    },
  },
  &quot;modelMonitoringSchema&quot;: { # The Model Monitoring Schema definition. # Monitoring Schema is to specify the model&#x27;s features, prediction outputs and ground truth properties. It is used to extract pertinent data from the dataset and to process features based on their properties. Make sure that the schema aligns with your dataset, if it does not, we will be unable to extract data from the dataset. It is required for most models, but optional for Vertex AI AutoML Tables unless the schem information is not available.
    &quot;featureFields&quot;: [ # Feature names of the model. Vertex AI will try to match the features from your dataset as follows: * For &#x27;csv&#x27; files, the header names are required, and we will extract the corresponding feature values when the header names align with the feature names. * For &#x27;jsonl&#x27; files, we will extract the corresponding feature values if the key names match the feature names. Note: Nested features are not supported, so please ensure your features are flattened. Ensure the feature values are scalar or an array of scalars. * For &#x27;bigquery&#x27; dataset, we will extract the corresponding feature values if the column names match the feature names. Note: The column type can be a scalar or an array of scalars. STRUCT or JSON types are not supported. You may use SQL queries to select or aggregate the relevant features from your original table. However, ensure that the &#x27;schema&#x27; of the query results meets our requirements. * For the Vertex AI Endpoint Request Response Logging table or Vertex AI Batch Prediction Job results. If the instance_type is an array, ensure that the sequence in feature_fields matches the order of features in the prediction instance. We will match the feature with the array in the order specified in [feature_fields].
      { # Schema field definition.
        &quot;dataType&quot;: &quot;A String&quot;, # Supported data types are: `float` `integer` `boolean` `string` `categorical`
        &quot;name&quot;: &quot;A String&quot;, # Field name.
        &quot;repeated&quot;: True or False, # Describes if the schema field is an array of given data type.
      },
    ],
    &quot;groundTruthFields&quot;: [ # Target /ground truth names of the model.
      { # Schema field definition.
        &quot;dataType&quot;: &quot;A String&quot;, # Supported data types are: `float` `integer` `boolean` `string` `categorical`
        &quot;name&quot;: &quot;A String&quot;, # Field name.
        &quot;repeated&quot;: True or False, # Describes if the schema field is an array of given data type.
      },
    ],
    &quot;predictionFields&quot;: [ # Prediction output names of the model. The requirements are the same as the feature_fields. For AutoML Tables, the prediction output name presented in schema will be: `predicted_{target_column}`, the `target_column` is the one you specified when you train the model. For Prediction output drift analysis: * AutoML Classification, the distribution of the argmax label will be analyzed. * AutoML Regression, the distribution of the value will be analyzed.
      { # Schema field definition.
        &quot;dataType&quot;: &quot;A String&quot;, # Supported data types are: `float` `integer` `boolean` `string` `categorical`
        &quot;name&quot;: &quot;A String&quot;, # Field name.
        &quot;repeated&quot;: True or False, # Describes if the schema field is an array of given data type.
      },
    ],
  },
  &quot;modelMonitoringTarget&quot;: { # The monitoring target refers to the entity that is subject to analysis. e.g. Vertex AI Model version. # The entity that is subject to analysis. Currently only models in Vertex AI Model Registry are supported. If you want to analyze the model which is outside the Vertex AI, you could register a model in Vertex AI Model Registry using just a display name.
    &quot;vertexModel&quot;: { # Model in Vertex AI Model Registry. # Model in Vertex AI Model Registry.
      &quot;model&quot;: &quot;A String&quot;, # Model resource name. Format: projects/{project}/locations/{location}/models/{model}.
      &quot;modelVersionId&quot;: &quot;A String&quot;, # Model version id.
    },
  },
  &quot;name&quot;: &quot;A String&quot;, # Immutable. Resource name of the ModelMonitor. Format: `projects/{project}/locations/{location}/modelMonitors/{model_monitor}`.
  &quot;notificationSpec&quot;: { # Notification spec(email, notification channel) for model monitoring statistics/alerts. # Optional default notification spec, it can be overridden in the ModelMonitoringJob notification spec.
    &quot;emailConfig&quot;: { # The config for email alerts. # Email alert config.
      &quot;userEmails&quot;: [ # The email addresses to send the alerts.
        &quot;A String&quot;,
      ],
    },
    &quot;enableCloudLogging&quot;: True or False, # Dump the anomalies to Cloud Logging. The anomalies will be put to json payload encoded from proto google.cloud.aiplatform.logging.ModelMonitoringAnomaliesLogEntry. This can be further sinked to Pub/Sub or any other services supported by Cloud Logging.
    &quot;notificationChannelConfigs&quot;: [ # Notification channel config.
      { # Google Cloud Notification Channel config.
        &quot;notificationChannel&quot;: &quot;A String&quot;, # Resource names of the NotificationChannels. Must be of the format `projects//notificationChannels/`
      },
    ],
  },
  &quot;outputSpec&quot;: { # Specification for the export destination of monitoring results, including metrics, logs, etc. # Optional default monitoring metrics/logs export spec, it can be overridden in the ModelMonitoringJob output spec. If not specified, a default Google Cloud Storage bucket will be created under your project.
    &quot;gcsBaseDirectory&quot;: { # The Google Cloud Storage location where the output is to be written to. # Google Cloud Storage base folder path for metrics, error logs, etc.
      &quot;outputUriPrefix&quot;: &quot;A String&quot;, # Required. Google Cloud Storage URI to output directory. If the uri doesn&#x27;t end with &#x27;/&#x27;, a &#x27;/&#x27; will be automatically appended. The directory is created if it doesn&#x27;t exist.
    },
  },
  &quot;satisfiesPzi&quot;: True or False, # Output only. Reserved for future use.
  &quot;satisfiesPzs&quot;: True or False, # Output only. Reserved for future use.
  &quot;tabularObjective&quot;: { # Tabular monitoring objective. # Optional default tabular model monitoring objective.
    &quot;featureAttributionSpec&quot;: { # Feature attribution monitoring spec. # Feature attribution monitoring spec.
      &quot;batchExplanationDedicatedResources&quot;: { # A description of resources that are used for performing batch operations, are dedicated to a Model, and need manual configuration. # The config of resources used by the Model Monitoring during the batch explanation for non-AutoML models. If not set, `n1-standard-2` machine type will be used by default.
        &quot;flexStart&quot;: { # FlexStart is used to schedule the deployment workload on DWS resource. It contains the max duration of the deployment. # Optional. Immutable. If set, use DWS resource to schedule the deployment workload. reference: (https://cloud.google.com/blog/products/compute/introducing-dynamic-workload-scheduler)
          &quot;maxRuntimeDuration&quot;: &quot;A String&quot;, # The max duration of the deployment is max_runtime_duration. The deployment will be terminated after the duration. The max_runtime_duration can be set up to 7 days.
        },
        &quot;machineSpec&quot;: { # Specification of a single machine. # Required. Immutable. The specification of a single machine.
          &quot;acceleratorCount&quot;: 42, # The number of accelerators to attach to the machine.
          &quot;acceleratorType&quot;: &quot;A String&quot;, # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
          &quot;gpuPartitionSize&quot;: &quot;A String&quot;, # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size=&quot;1g.10gb&quot;, the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1.
          &quot;machineType&quot;: &quot;A String&quot;, # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
          &quot;multihostGpuNodeCount&quot;: 42, # Optional. Immutable. The number of nodes per replica for multihost GPU deployments.
          &quot;reservationAffinity&quot;: { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
            &quot;key&quot;: &quot;A String&quot;, # Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use `compute.googleapis.com/reservation-name` as the key and specify the name of your reservation as its value.
            &quot;reservationAffinityType&quot;: &quot;A String&quot;, # Required. Specifies the reservation affinity type.
            &quot;values&quot;: [ # Optional. Corresponds to the label values of a reservation resource. This must be the full resource name of the reservation or reservation block.
              &quot;A String&quot;,
            ],
          },
          &quot;tpuTopology&quot;: &quot;A String&quot;, # Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: &quot;2x2x1&quot;).
        },
        &quot;maxReplicaCount&quot;: 42, # Immutable. The maximum number of machine replicas the batch operation may be scaled to. The default value is 10.
        &quot;spot&quot;: True or False, # Optional. If true, schedule the deployment workload on [spot VMs](https://cloud.google.com/kubernetes-engine/docs/concepts/spot-vms).
        &quot;startingReplicaCount&quot;: 42, # Immutable. The number of machine replicas used at the start of the batch operation. If not set, Vertex AI decides starting number, not greater than max_replica_count
      },
      &quot;defaultAlertCondition&quot;: { # Monitoring alert triggered condition. # Default alert condition for all the features.
        &quot;threshold&quot;: 3.14, # A condition that compares a stats value against a threshold. Alert will be triggered if value above the threshold.
      },
      &quot;featureAlertConditions&quot;: { # Per feature alert condition will override default alert condition.
        &quot;a_key&quot;: { # Monitoring alert triggered condition.
          &quot;threshold&quot;: 3.14, # A condition that compares a stats value against a threshold. Alert will be triggered if value above the threshold.
        },
      },
      &quot;features&quot;: [ # Feature names interested in monitoring. These should be a subset of the input feature names specified in the monitoring schema. If the field is not specified all features outlied in the monitoring schema will be used.
        &quot;A String&quot;,
      ],
    },
    &quot;featureDriftSpec&quot;: { # Data drift monitoring spec. Data drift measures the distribution distance between the current dataset and a baseline dataset. A typical use case is to detect data drift between the recent production serving dataset and the training dataset, or to compare the recent production dataset with a dataset from a previous period. # Input feature distribution drift monitoring spec.
      &quot;categoricalMetricType&quot;: &quot;A String&quot;, # Supported metrics type: * l_infinity * jensen_shannon_divergence
      &quot;defaultCategoricalAlertCondition&quot;: { # Monitoring alert triggered condition. # Default alert condition for all the categorical features.
        &quot;threshold&quot;: 3.14, # A condition that compares a stats value against a threshold. Alert will be triggered if value above the threshold.
      },
      &quot;defaultNumericAlertCondition&quot;: { # Monitoring alert triggered condition. # Default alert condition for all the numeric features.
        &quot;threshold&quot;: 3.14, # A condition that compares a stats value against a threshold. Alert will be triggered if value above the threshold.
      },
      &quot;featureAlertConditions&quot;: { # Per feature alert condition will override default alert condition.
        &quot;a_key&quot;: { # Monitoring alert triggered condition.
          &quot;threshold&quot;: 3.14, # A condition that compares a stats value against a threshold. Alert will be triggered if value above the threshold.
        },
      },
      &quot;features&quot;: [ # Feature names / Prediction output names interested in monitoring. These should be a subset of the input feature names or prediction output names specified in the monitoring schema. If the field is not specified all features / prediction outputs outlied in the monitoring schema will be used.
        &quot;A String&quot;,
      ],
      &quot;numericMetricType&quot;: &quot;A String&quot;, # Supported metrics type: * jensen_shannon_divergence
    },
    &quot;predictionOutputDriftSpec&quot;: { # Data drift monitoring spec. Data drift measures the distribution distance between the current dataset and a baseline dataset. A typical use case is to detect data drift between the recent production serving dataset and the training dataset, or to compare the recent production dataset with a dataset from a previous period. # Prediction output distribution drift monitoring spec.
      &quot;categoricalMetricType&quot;: &quot;A String&quot;, # Supported metrics type: * l_infinity * jensen_shannon_divergence
      &quot;defaultCategoricalAlertCondition&quot;: { # Monitoring alert triggered condition. # Default alert condition for all the categorical features.
        &quot;threshold&quot;: 3.14, # A condition that compares a stats value against a threshold. Alert will be triggered if value above the threshold.
      },
      &quot;defaultNumericAlertCondition&quot;: { # Monitoring alert triggered condition. # Default alert condition for all the numeric features.
        &quot;threshold&quot;: 3.14, # A condition that compares a stats value against a threshold. Alert will be triggered if value above the threshold.
      },
      &quot;featureAlertConditions&quot;: { # Per feature alert condition will override default alert condition.
        &quot;a_key&quot;: { # Monitoring alert triggered condition.
          &quot;threshold&quot;: 3.14, # A condition that compares a stats value against a threshold. Alert will be triggered if value above the threshold.
        },
      },
      &quot;features&quot;: [ # Feature names / Prediction output names interested in monitoring. These should be a subset of the input feature names or prediction output names specified in the monitoring schema. If the field is not specified all features / prediction outputs outlied in the monitoring schema will be used.
        &quot;A String&quot;,
      ],
      &quot;numericMetricType&quot;: &quot;A String&quot;, # Supported metrics type: * jensen_shannon_divergence
    },
  },
  &quot;trainingDataset&quot;: { # Model monitoring data input spec. # Optional training dataset used to train the model. It can serve as a reference dataset to identify changes in production.
    &quot;batchPredictionOutput&quot;: { # Data from Vertex AI Batch prediction job output. # Vertex AI Batch prediction Job.
      &quot;batchPredictionJob&quot;: &quot;A String&quot;, # Vertex AI Batch prediction job resource name. The job must match the model version specified in [ModelMonitor].[model_monitoring_target].
    },
    &quot;columnizedDataset&quot;: { # Input dataset spec. # Columnized dataset.
      &quot;bigquerySource&quot;: { # Dataset spec for data sotred in BigQuery. # BigQuery data source.
        &quot;query&quot;: &quot;A String&quot;, # Standard SQL to be used instead of the `table_uri`.
        &quot;tableUri&quot;: &quot;A String&quot;, # BigQuery URI to a table, up to 2000 characters long. All the columns in the table will be selected. Accepted forms: * BigQuery path. For example: `bq://projectId.bqDatasetId.bqTableId`.
      },
      &quot;gcsSource&quot;: { # Dataset spec for data stored in Google Cloud Storage. # Google Cloud Storage data source.
        &quot;format&quot;: &quot;A String&quot;, # Data format of the dataset.
        &quot;gcsUri&quot;: &quot;A String&quot;, # Google Cloud Storage URI to the input file(s). May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/wildcards.
      },
      &quot;timestampField&quot;: &quot;A String&quot;, # The timestamp field. Usually for serving data.
      &quot;vertexDataset&quot;: &quot;A String&quot;, # Resource name of the Vertex AI managed dataset.
    },
    &quot;timeInterval&quot;: { # Represents a time interval, encoded as a Timestamp start (inclusive) and a Timestamp end (exclusive). The start must be less than or equal to the end. When the start equals the end, the interval is empty (matches no time). When both start and end are unspecified, the interval matches any time. # The time interval (pair of start_time and end_time) for which results should be returned.
      &quot;endTime&quot;: &quot;A String&quot;, # Optional. Exclusive end of the interval. If specified, a Timestamp matching this interval will have to be before the end.
      &quot;startTime&quot;: &quot;A String&quot;, # Optional. Inclusive start of the interval. If specified, a Timestamp matching this interval will have to be the same or after the start.
    },
    &quot;timeOffset&quot;: { # Time offset setting. # The time offset setting for which results should be returned.
      &quot;offset&quot;: &quot;A String&quot;, # [offset] is the time difference from the cut-off time. For scheduled jobs, the cut-off time is the scheduled time. For non-scheduled jobs, it&#x27;s the time when the job was created. Currently we support the following format: &#x27;w|W&#x27;: Week, &#x27;d|D&#x27;: Day, &#x27;h|H&#x27;: Hour E.g. &#x27;1h&#x27; stands for 1 hour, &#x27;2d&#x27; stands for 2 days.
      &quot;window&quot;: &quot;A String&quot;, # [window] refers to the scope of data selected for analysis. It allows you to specify the quantity of data you wish to examine. Currently we support the following format: &#x27;w|W&#x27;: Week, &#x27;d|D&#x27;: Day, &#x27;h|H&#x27;: Hour E.g. &#x27;1h&#x27; stands for 1 hour, &#x27;2d&#x27; stands for 2 days.
    },
    &quot;vertexEndpointLogs&quot;: { # Data from Vertex AI Endpoint request response logging. # Vertex AI Endpoint request &amp; response logging.
      &quot;endpoints&quot;: [ # List of endpoint resource names. The endpoints must enable the logging with the [Endpoint].[request_response_logging_config], and must contain the deployed model corresponding to the model version specified in [ModelMonitor].[model_monitoring_target].
        &quot;A String&quot;,
      ],
    },
  },
  &quot;updateTime&quot;: &quot;A String&quot;, # Output only. Timestamp when this ModelMonitor was updated most recently.
}

  modelMonitorId: string, Optional. The ID to use for the Model Monitor, which will become the final component of the model monitor resource name. The maximum length is 63 characters, and valid characters are `/^[a-z]([a-z0-9-]{0,61}[a-z0-9])?$/`.
  x__xgafv: string, V1 error format.
    Allowed values
      1 - v1 error format
      2 - v2 error format

Returns:
  An object of the form:

    { # This resource represents a long-running operation that is the result of a network API call.
  &quot;done&quot;: True or False, # If the value is `false`, it means the operation is still in progress. If `true`, the operation is completed, and either `error` or `response` is available.
  &quot;error&quot;: { # The `Status` type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by [gRPC](https://github.com/grpc). Each `Status` message contains three pieces of data: error code, error message, and error details. You can find out more about this error model and how to work with it in the [API Design Guide](https://cloud.google.com/apis/design/errors). # The error result of the operation in case of failure or cancellation.
    &quot;code&quot;: 42, # The status code, which should be an enum value of google.rpc.Code.
    &quot;details&quot;: [ # A list of messages that carry the error details. There is a common set of message types for APIs to use.
      {
        &quot;a_key&quot;: &quot;&quot;, # Properties of the object. Contains field @type with type URL.
      },
    ],
    &quot;message&quot;: &quot;A String&quot;, # A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.
  },
  &quot;metadata&quot;: { # Service-specific metadata associated with the operation. It typically contains progress information and common metadata such as create time. Some services might not provide such metadata. Any method that returns a long-running operation should document the metadata type, if any.
    &quot;a_key&quot;: &quot;&quot;, # Properties of the object. Contains field @type with type URL.
  },
  &quot;name&quot;: &quot;A String&quot;, # The server-assigned name, which is only unique within the same service that originally returns it. If you use the default HTTP mapping, the `name` should be a resource name ending with `operations/{unique_id}`.
  &quot;response&quot;: { # The normal, successful response of the operation. If the original method returns no data on success, such as `Delete`, the response is `google.protobuf.Empty`. If the original method is standard `Get`/`Create`/`Update`, the response should be the resource. For other methods, the response should have the type `XxxResponse`, where `Xxx` is the original method name. For example, if the original method name is `TakeSnapshot()`, the inferred response type is `TakeSnapshotResponse`.
    &quot;a_key&quot;: &quot;&quot;, # Properties of the object. Contains field @type with type URL.
  },
}</pre>
</div>

<div class="method">
    <code class="details" id="delete">delete(name, force=None, x__xgafv=None)</code>
  <pre>Deletes a ModelMonitor.

Args:
  name: string, Required. The name of the ModelMonitor resource to be deleted. Format: `projects/{project}/locations/{location}/modelMonitords/{model_monitor}` (required)
  force: boolean, Optional. Force delete the model monitor with schedules.
  x__xgafv: string, V1 error format.
    Allowed values
      1 - v1 error format
      2 - v2 error format

Returns:
  An object of the form:

    { # This resource represents a long-running operation that is the result of a network API call.
  &quot;done&quot;: True or False, # If the value is `false`, it means the operation is still in progress. If `true`, the operation is completed, and either `error` or `response` is available.
  &quot;error&quot;: { # The `Status` type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by [gRPC](https://github.com/grpc). Each `Status` message contains three pieces of data: error code, error message, and error details. You can find out more about this error model and how to work with it in the [API Design Guide](https://cloud.google.com/apis/design/errors). # The error result of the operation in case of failure or cancellation.
    &quot;code&quot;: 42, # The status code, which should be an enum value of google.rpc.Code.
    &quot;details&quot;: [ # A list of messages that carry the error details. There is a common set of message types for APIs to use.
      {
        &quot;a_key&quot;: &quot;&quot;, # Properties of the object. Contains field @type with type URL.
      },
    ],
    &quot;message&quot;: &quot;A String&quot;, # A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.
  },
  &quot;metadata&quot;: { # Service-specific metadata associated with the operation. It typically contains progress information and common metadata such as create time. Some services might not provide such metadata. Any method that returns a long-running operation should document the metadata type, if any.
    &quot;a_key&quot;: &quot;&quot;, # Properties of the object. Contains field @type with type URL.
  },
  &quot;name&quot;: &quot;A String&quot;, # The server-assigned name, which is only unique within the same service that originally returns it. If you use the default HTTP mapping, the `name` should be a resource name ending with `operations/{unique_id}`.
  &quot;response&quot;: { # The normal, successful response of the operation. If the original method returns no data on success, such as `Delete`, the response is `google.protobuf.Empty`. If the original method is standard `Get`/`Create`/`Update`, the response should be the resource. For other methods, the response should have the type `XxxResponse`, where `Xxx` is the original method name. For example, if the original method name is `TakeSnapshot()`, the inferred response type is `TakeSnapshotResponse`.
    &quot;a_key&quot;: &quot;&quot;, # Properties of the object. Contains field @type with type URL.
  },
}</pre>
</div>

<div class="method">
    <code class="details" id="get">get(name, x__xgafv=None)</code>
  <pre>Gets a ModelMonitor.

Args:
  name: string, Required. The name of the ModelMonitor resource. Format: `projects/{project}/locations/{location}/modelMonitors/{model_monitor}` (required)
  x__xgafv: string, V1 error format.
    Allowed values
      1 - v1 error format
      2 - v2 error format

Returns:
  An object of the form:

    { # Vertex AI Model Monitoring Service serves as a central hub for the analysis and visualization of data quality and performance related to models. ModelMonitor stands as a top level resource for overseeing your model monitoring tasks.
  &quot;createTime&quot;: &quot;A String&quot;, # Output only. Timestamp when this ModelMonitor was created.
  &quot;displayName&quot;: &quot;A String&quot;, # The display name of the ModelMonitor. The name can be up to 128 characters long and can consist of any UTF-8.
  &quot;encryptionSpec&quot;: { # Represents a customer-managed encryption key spec that can be applied to a top-level resource. # Customer-managed encryption key spec for a ModelMonitor. If set, this ModelMonitor and all sub-resources of this ModelMonitor will be secured by this key.
    &quot;kmsKeyName&quot;: &quot;A String&quot;, # Required. The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: `projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key`. The key needs to be in the same region as where the compute resource is created.
  },
  &quot;explanationSpec&quot;: { # Specification of Model explanation. # Optional model explanation spec. It is used for feature attribution monitoring.
    &quot;metadata&quot;: { # Metadata describing the Model&#x27;s input and output for explanation. # Optional. Metadata describing the Model&#x27;s input and output for explanation.
      &quot;featureAttributionsSchemaUri&quot;: &quot;A String&quot;, # Points to a YAML file stored on Google Cloud Storage describing the format of the feature attributions. The schema is defined as an OpenAPI 3.0.2 [Schema Object](https://github.com/OAI/OpenAPI-Specification/blob/main/versions/3.0.2.md#schemaObject). AutoML tabular Models always have this field populated by Vertex AI. Note: The URI given on output may be different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
      &quot;inputs&quot;: { # Required. Map from feature names to feature input metadata. Keys are the name of the features. Values are the specification of the feature. An empty InputMetadata is valid. It describes a text feature which has the name specified as the key in ExplanationMetadata.inputs. The baseline of the empty feature is chosen by Vertex AI. For Vertex AI-provided Tensorflow images, the key can be any friendly name of the feature. Once specified, featureAttributions are keyed by this key (if not grouped with another feature). For custom images, the key must match with the key in instance.
        &quot;a_key&quot;: { # Metadata of the input of a feature. Fields other than InputMetadata.input_baselines are applicable only for Models that are using Vertex AI-provided images for Tensorflow.
          &quot;denseShapeTensorName&quot;: &quot;A String&quot;, # Specifies the shape of the values of the input if the input is a sparse representation. Refer to Tensorflow documentation for more details: https://www.tensorflow.org/api_docs/python/tf/sparse/SparseTensor.
          &quot;encodedBaselines&quot;: [ # A list of baselines for the encoded tensor. The shape of each baseline should match the shape of the encoded tensor. If a scalar is provided, Vertex AI broadcasts to the same shape as the encoded tensor.
            &quot;&quot;,
          ],
          &quot;encodedTensorName&quot;: &quot;A String&quot;, # Encoded tensor is a transformation of the input tensor. Must be provided if choosing Integrated Gradients attribution or XRAI attribution and the input tensor is not differentiable. An encoded tensor is generated if the input tensor is encoded by a lookup table.
          &quot;encoding&quot;: &quot;A String&quot;, # Defines how the feature is encoded into the input tensor. Defaults to IDENTITY.
          &quot;featureValueDomain&quot;: { # Domain details of the input feature value. Provides numeric information about the feature, such as its range (min, max). If the feature has been pre-processed, for example with z-scoring, then it provides information about how to recover the original feature. For example, if the input feature is an image and it has been pre-processed to obtain 0-mean and stddev = 1 values, then original_mean, and original_stddev refer to the mean and stddev of the original feature (e.g. image tensor) from which input feature (with mean = 0 and stddev = 1) was obtained. # The domain details of the input feature value. Like min/max, original mean or standard deviation if normalized.
            &quot;maxValue&quot;: 3.14, # The maximum permissible value for this feature.
            &quot;minValue&quot;: 3.14, # The minimum permissible value for this feature.
            &quot;originalMean&quot;: 3.14, # If this input feature has been normalized to a mean value of 0, the original_mean specifies the mean value of the domain prior to normalization.
            &quot;originalStddev&quot;: 3.14, # If this input feature has been normalized to a standard deviation of 1.0, the original_stddev specifies the standard deviation of the domain prior to normalization.
          },
          &quot;groupName&quot;: &quot;A String&quot;, # Name of the group that the input belongs to. Features with the same group name will be treated as one feature when computing attributions. Features grouped together can have different shapes in value. If provided, there will be one single attribution generated in Attribution.feature_attributions, keyed by the group name.
          &quot;indexFeatureMapping&quot;: [ # A list of feature names for each index in the input tensor. Required when the input InputMetadata.encoding is BAG_OF_FEATURES, BAG_OF_FEATURES_SPARSE, INDICATOR.
            &quot;A String&quot;,
          ],
          &quot;indicesTensorName&quot;: &quot;A String&quot;, # Specifies the index of the values of the input tensor. Required when the input tensor is a sparse representation. Refer to Tensorflow documentation for more details: https://www.tensorflow.org/api_docs/python/tf/sparse/SparseTensor.
          &quot;inputBaselines&quot;: [ # Baseline inputs for this feature. If no baseline is specified, Vertex AI chooses the baseline for this feature. If multiple baselines are specified, Vertex AI returns the average attributions across them in Attribution.feature_attributions. For Vertex AI-provided Tensorflow images (both 1.x and 2.x), the shape of each baseline must match the shape of the input tensor. If a scalar is provided, we broadcast to the same shape as the input tensor. For custom images, the element of the baselines must be in the same format as the feature&#x27;s input in the instance[]. The schema of any single instance may be specified via Endpoint&#x27;s DeployedModels&#x27; Model&#x27;s PredictSchemata&#x27;s instance_schema_uri.
            &quot;&quot;,
          ],
          &quot;inputTensorName&quot;: &quot;A String&quot;, # Name of the input tensor for this feature. Required and is only applicable to Vertex AI-provided images for Tensorflow.
          &quot;modality&quot;: &quot;A String&quot;, # Modality of the feature. Valid values are: numeric, image. Defaults to numeric.
          &quot;visualization&quot;: { # Visualization configurations for image explanation. # Visualization configurations for image explanation.
            &quot;clipPercentLowerbound&quot;: 3.14, # Excludes attributions below the specified percentile, from the highlighted areas. Defaults to 62.
            &quot;clipPercentUpperbound&quot;: 3.14, # Excludes attributions above the specified percentile from the highlighted areas. Using the clip_percent_upperbound and clip_percent_lowerbound together can be useful for filtering out noise and making it easier to see areas of strong attribution. Defaults to 99.9.
            &quot;colorMap&quot;: &quot;A String&quot;, # The color scheme used for the highlighted areas. Defaults to PINK_GREEN for Integrated Gradients attribution, which shows positive attributions in green and negative in pink. Defaults to VIRIDIS for XRAI attribution, which highlights the most influential regions in yellow and the least influential in blue.
            &quot;overlayType&quot;: &quot;A String&quot;, # How the original image is displayed in the visualization. Adjusting the overlay can help increase visual clarity if the original image makes it difficult to view the visualization. Defaults to NONE.
            &quot;polarity&quot;: &quot;A String&quot;, # Whether to only highlight pixels with positive contributions, negative or both. Defaults to POSITIVE.
            &quot;type&quot;: &quot;A String&quot;, # Type of the image visualization. Only applicable to Integrated Gradients attribution. OUTLINES shows regions of attribution, while PIXELS shows per-pixel attribution. Defaults to OUTLINES.
          },
        },
      },
      &quot;latentSpaceSource&quot;: &quot;A String&quot;, # Name of the source to generate embeddings for example based explanations.
      &quot;outputs&quot;: { # Required. Map from output names to output metadata. For Vertex AI-provided Tensorflow images, keys can be any user defined string that consists of any UTF-8 characters. For custom images, keys are the name of the output field in the prediction to be explained. Currently only one key is allowed.
        &quot;a_key&quot;: { # Metadata of the prediction output to be explained.
          &quot;displayNameMappingKey&quot;: &quot;A String&quot;, # Specify a field name in the prediction to look for the display name. Use this if the prediction contains the display names for the outputs. The display names in the prediction must have the same shape of the outputs, so that it can be located by Attribution.output_index for a specific output.
          &quot;indexDisplayNameMapping&quot;: &quot;&quot;, # Static mapping between the index and display name. Use this if the outputs are a deterministic n-dimensional array, e.g. a list of scores of all the classes in a pre-defined order for a multi-classification Model. It&#x27;s not feasible if the outputs are non-deterministic, e.g. the Model produces top-k classes or sort the outputs by their values. The shape of the value must be an n-dimensional array of strings. The number of dimensions must match that of the outputs to be explained. The Attribution.output_display_name is populated by locating in the mapping with Attribution.output_index.
          &quot;outputTensorName&quot;: &quot;A String&quot;, # Name of the output tensor. Required and is only applicable to Vertex AI provided images for Tensorflow.
        },
      },
    },
    &quot;parameters&quot;: { # Parameters to configure explaining for Model&#x27;s predictions. # Required. Parameters that configure explaining of the Model&#x27;s predictions.
      &quot;examples&quot;: { # Example-based explainability that returns the nearest neighbors from the provided dataset. # Example-based explanations that returns the nearest neighbors from the provided dataset.
        &quot;exampleGcsSource&quot;: { # The Cloud Storage input instances. # The Cloud Storage input instances.
          &quot;dataFormat&quot;: &quot;A String&quot;, # The format in which instances are given, if not specified, assume it&#x27;s JSONL format. Currently only JSONL format is supported.
          &quot;gcsSource&quot;: { # The Google Cloud Storage location for the input content. # The Cloud Storage location for the input instances.
            &quot;uris&quot;: [ # Required. Google Cloud Storage URI(-s) to the input file(s). May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/wildcards.
              &quot;A String&quot;,
            ],
          },
        },
        &quot;gcsSource&quot;: { # The Google Cloud Storage location for the input content. # The Cloud Storage locations that contain the instances to be indexed for approximate nearest neighbor search.
          &quot;uris&quot;: [ # Required. Google Cloud Storage URI(-s) to the input file(s). May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/wildcards.
            &quot;A String&quot;,
          ],
        },
        &quot;nearestNeighborSearchConfig&quot;: &quot;&quot;, # The full configuration for the generated index, the semantics are the same as metadata and should match [NearestNeighborSearchConfig](https://cloud.google.com/vertex-ai/docs/explainable-ai/configuring-explanations-example-based#nearest-neighbor-search-config).
        &quot;neighborCount&quot;: 42, # The number of neighbors to return when querying for examples.
        &quot;presets&quot;: { # Preset configuration for example-based explanations # Simplified preset configuration, which automatically sets configuration values based on the desired query speed-precision trade-off and modality.
          &quot;modality&quot;: &quot;A String&quot;, # The modality of the uploaded model, which automatically configures the distance measurement and feature normalization for the underlying example index and queries. If your model does not precisely fit one of these types, it is okay to choose the closest type.
          &quot;query&quot;: &quot;A String&quot;, # Preset option controlling parameters for speed-precision trade-off when querying for examples. If omitted, defaults to `PRECISE`.
        },
      },
      &quot;integratedGradientsAttribution&quot;: { # An attribution method that computes the Aumann-Shapley value taking advantage of the model&#x27;s fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1703.01365 # An attribution method that computes Aumann-Shapley values taking advantage of the model&#x27;s fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1703.01365
        &quot;blurBaselineConfig&quot;: { # Config for blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383 # Config for IG with blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383
          &quot;maxBlurSigma&quot;: 3.14, # The standard deviation of the blur kernel for the blurred baseline. The same blurring parameter is used for both the height and the width dimension. If not set, the method defaults to the zero (i.e. black for images) baseline.
        },
        &quot;smoothGradConfig&quot;: { # Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf # Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
          &quot;featureNoiseSigma&quot;: { # Noise sigma by features. Noise sigma represents the standard deviation of the gaussian kernel that will be used to add noise to interpolated inputs prior to computing gradients. # This is similar to noise_sigma, but provides additional flexibility. A separate noise sigma can be provided for each feature, which is useful if their distributions are different. No noise is added to features that are not set. If this field is unset, noise_sigma will be used for all features.
            &quot;noiseSigma&quot;: [ # Noise sigma per feature. No noise is added to features that are not set.
              { # Noise sigma for a single feature.
                &quot;name&quot;: &quot;A String&quot;, # The name of the input feature for which noise sigma is provided. The features are defined in explanation metadata inputs.
                &quot;sigma&quot;: 3.14, # This represents the standard deviation of the Gaussian kernel that will be used to add noise to the feature prior to computing gradients. Similar to noise_sigma but represents the noise added to the current feature. Defaults to 0.1.
              },
            ],
          },
          &quot;noiseSigma&quot;: 3.14, # This is a single float value and will be used to add noise to all the features. Use this field when all features are normalized to have the same distribution: scale to range [0, 1], [-1, 1] or z-scoring, where features are normalized to have 0-mean and 1-variance. Learn more about [normalization](https://developers.google.com/machine-learning/data-prep/transform/normalization). For best results the recommended value is about 10% - 20% of the standard deviation of the input feature. Refer to section 3.2 of the SmoothGrad paper: https://arxiv.org/pdf/1706.03825.pdf. Defaults to 0.1. If the distribution is different per feature, set feature_noise_sigma instead for each feature.
          &quot;noisySampleCount&quot;: 42, # The number of gradient samples to use for approximation. The higher this number, the more accurate the gradient is, but the runtime complexity increases by this factor as well. Valid range of its value is [1, 50]. Defaults to 3.
        },
        &quot;stepCount&quot;: 42, # Required. The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is within the desired error range. Valid range of its value is [1, 100], inclusively.
      },
      &quot;outputIndices&quot;: [ # If populated, only returns attributions that have output_index contained in output_indices. It must be an ndarray of integers, with the same shape of the output it&#x27;s explaining. If not populated, returns attributions for top_k indices of outputs. If neither top_k nor output_indices is populated, returns the argmax index of the outputs. Only applicable to Models that predict multiple outputs (e,g, multi-class Models that predict multiple classes).
        &quot;&quot;,
      ],
      &quot;sampledShapleyAttribution&quot;: { # An attribution method that approximates Shapley values for features that contribute to the label being predicted. A sampling strategy is used to approximate the value rather than considering all subsets of features. # An attribution method that approximates Shapley values for features that contribute to the label being predicted. A sampling strategy is used to approximate the value rather than considering all subsets of features. Refer to this paper for model details: https://arxiv.org/abs/1306.4265.
        &quot;pathCount&quot;: 42, # Required. The number of feature permutations to consider when approximating the Shapley values. Valid range of its value is [1, 50], inclusively.
      },
      &quot;topK&quot;: 42, # If populated, returns attributions for top K indices of outputs (defaults to 1). Only applies to Models that predicts more than one outputs (e,g, multi-class Models). When set to -1, returns explanations for all outputs.
      &quot;xraiAttribution&quot;: { # An explanation method that redistributes Integrated Gradients attributions to segmented regions, taking advantage of the model&#x27;s fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1906.02825 Supported only by image Models. # An attribution method that redistributes Integrated Gradients attribution to segmented regions, taking advantage of the model&#x27;s fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1906.02825 XRAI currently performs better on natural images, like a picture of a house or an animal. If the images are taken in artificial environments, like a lab or manufacturing line, or from diagnostic equipment, like x-rays or quality-control cameras, use Integrated Gradients instead.
        &quot;blurBaselineConfig&quot;: { # Config for blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383 # Config for XRAI with blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383
          &quot;maxBlurSigma&quot;: 3.14, # The standard deviation of the blur kernel for the blurred baseline. The same blurring parameter is used for both the height and the width dimension. If not set, the method defaults to the zero (i.e. black for images) baseline.
        },
        &quot;smoothGradConfig&quot;: { # Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf # Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
          &quot;featureNoiseSigma&quot;: { # Noise sigma by features. Noise sigma represents the standard deviation of the gaussian kernel that will be used to add noise to interpolated inputs prior to computing gradients. # This is similar to noise_sigma, but provides additional flexibility. A separate noise sigma can be provided for each feature, which is useful if their distributions are different. No noise is added to features that are not set. If this field is unset, noise_sigma will be used for all features.
            &quot;noiseSigma&quot;: [ # Noise sigma per feature. No noise is added to features that are not set.
              { # Noise sigma for a single feature.
                &quot;name&quot;: &quot;A String&quot;, # The name of the input feature for which noise sigma is provided. The features are defined in explanation metadata inputs.
                &quot;sigma&quot;: 3.14, # This represents the standard deviation of the Gaussian kernel that will be used to add noise to the feature prior to computing gradients. Similar to noise_sigma but represents the noise added to the current feature. Defaults to 0.1.
              },
            ],
          },
          &quot;noiseSigma&quot;: 3.14, # This is a single float value and will be used to add noise to all the features. Use this field when all features are normalized to have the same distribution: scale to range [0, 1], [-1, 1] or z-scoring, where features are normalized to have 0-mean and 1-variance. Learn more about [normalization](https://developers.google.com/machine-learning/data-prep/transform/normalization). For best results the recommended value is about 10% - 20% of the standard deviation of the input feature. Refer to section 3.2 of the SmoothGrad paper: https://arxiv.org/pdf/1706.03825.pdf. Defaults to 0.1. If the distribution is different per feature, set feature_noise_sigma instead for each feature.
          &quot;noisySampleCount&quot;: 42, # The number of gradient samples to use for approximation. The higher this number, the more accurate the gradient is, but the runtime complexity increases by this factor as well. Valid range of its value is [1, 50]. Defaults to 3.
        },
        &quot;stepCount&quot;: 42, # Required. The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is met within the desired error range. Valid range of its value is [1, 100], inclusively.
      },
    },
  },
  &quot;modelMonitoringSchema&quot;: { # The Model Monitoring Schema definition. # Monitoring Schema is to specify the model&#x27;s features, prediction outputs and ground truth properties. It is used to extract pertinent data from the dataset and to process features based on their properties. Make sure that the schema aligns with your dataset, if it does not, we will be unable to extract data from the dataset. It is required for most models, but optional for Vertex AI AutoML Tables unless the schem information is not available.
    &quot;featureFields&quot;: [ # Feature names of the model. Vertex AI will try to match the features from your dataset as follows: * For &#x27;csv&#x27; files, the header names are required, and we will extract the corresponding feature values when the header names align with the feature names. * For &#x27;jsonl&#x27; files, we will extract the corresponding feature values if the key names match the feature names. Note: Nested features are not supported, so please ensure your features are flattened. Ensure the feature values are scalar or an array of scalars. * For &#x27;bigquery&#x27; dataset, we will extract the corresponding feature values if the column names match the feature names. Note: The column type can be a scalar or an array of scalars. STRUCT or JSON types are not supported. You may use SQL queries to select or aggregate the relevant features from your original table. However, ensure that the &#x27;schema&#x27; of the query results meets our requirements. * For the Vertex AI Endpoint Request Response Logging table or Vertex AI Batch Prediction Job results. If the instance_type is an array, ensure that the sequence in feature_fields matches the order of features in the prediction instance. We will match the feature with the array in the order specified in [feature_fields].
      { # Schema field definition.
        &quot;dataType&quot;: &quot;A String&quot;, # Supported data types are: `float` `integer` `boolean` `string` `categorical`
        &quot;name&quot;: &quot;A String&quot;, # Field name.
        &quot;repeated&quot;: True or False, # Describes if the schema field is an array of given data type.
      },
    ],
    &quot;groundTruthFields&quot;: [ # Target /ground truth names of the model.
      { # Schema field definition.
        &quot;dataType&quot;: &quot;A String&quot;, # Supported data types are: `float` `integer` `boolean` `string` `categorical`
        &quot;name&quot;: &quot;A String&quot;, # Field name.
        &quot;repeated&quot;: True or False, # Describes if the schema field is an array of given data type.
      },
    ],
    &quot;predictionFields&quot;: [ # Prediction output names of the model. The requirements are the same as the feature_fields. For AutoML Tables, the prediction output name presented in schema will be: `predicted_{target_column}`, the `target_column` is the one you specified when you train the model. For Prediction output drift analysis: * AutoML Classification, the distribution of the argmax label will be analyzed. * AutoML Regression, the distribution of the value will be analyzed.
      { # Schema field definition.
        &quot;dataType&quot;: &quot;A String&quot;, # Supported data types are: `float` `integer` `boolean` `string` `categorical`
        &quot;name&quot;: &quot;A String&quot;, # Field name.
        &quot;repeated&quot;: True or False, # Describes if the schema field is an array of given data type.
      },
    ],
  },
  &quot;modelMonitoringTarget&quot;: { # The monitoring target refers to the entity that is subject to analysis. e.g. Vertex AI Model version. # The entity that is subject to analysis. Currently only models in Vertex AI Model Registry are supported. If you want to analyze the model which is outside the Vertex AI, you could register a model in Vertex AI Model Registry using just a display name.
    &quot;vertexModel&quot;: { # Model in Vertex AI Model Registry. # Model in Vertex AI Model Registry.
      &quot;model&quot;: &quot;A String&quot;, # Model resource name. Format: projects/{project}/locations/{location}/models/{model}.
      &quot;modelVersionId&quot;: &quot;A String&quot;, # Model version id.
    },
  },
  &quot;name&quot;: &quot;A String&quot;, # Immutable. Resource name of the ModelMonitor. Format: `projects/{project}/locations/{location}/modelMonitors/{model_monitor}`.
  &quot;notificationSpec&quot;: { # Notification spec(email, notification channel) for model monitoring statistics/alerts. # Optional default notification spec, it can be overridden in the ModelMonitoringJob notification spec.
    &quot;emailConfig&quot;: { # The config for email alerts. # Email alert config.
      &quot;userEmails&quot;: [ # The email addresses to send the alerts.
        &quot;A String&quot;,
      ],
    },
    &quot;enableCloudLogging&quot;: True or False, # Dump the anomalies to Cloud Logging. The anomalies will be put to json payload encoded from proto google.cloud.aiplatform.logging.ModelMonitoringAnomaliesLogEntry. This can be further sinked to Pub/Sub or any other services supported by Cloud Logging.
    &quot;notificationChannelConfigs&quot;: [ # Notification channel config.
      { # Google Cloud Notification Channel config.
        &quot;notificationChannel&quot;: &quot;A String&quot;, # Resource names of the NotificationChannels. Must be of the format `projects//notificationChannels/`
      },
    ],
  },
  &quot;outputSpec&quot;: { # Specification for the export destination of monitoring results, including metrics, logs, etc. # Optional default monitoring metrics/logs export spec, it can be overridden in the ModelMonitoringJob output spec. If not specified, a default Google Cloud Storage bucket will be created under your project.
    &quot;gcsBaseDirectory&quot;: { # The Google Cloud Storage location where the output is to be written to. # Google Cloud Storage base folder path for metrics, error logs, etc.
      &quot;outputUriPrefix&quot;: &quot;A String&quot;, # Required. Google Cloud Storage URI to output directory. If the uri doesn&#x27;t end with &#x27;/&#x27;, a &#x27;/&#x27; will be automatically appended. The directory is created if it doesn&#x27;t exist.
    },
  },
  &quot;satisfiesPzi&quot;: True or False, # Output only. Reserved for future use.
  &quot;satisfiesPzs&quot;: True or False, # Output only. Reserved for future use.
  &quot;tabularObjective&quot;: { # Tabular monitoring objective. # Optional default tabular model monitoring objective.
    &quot;featureAttributionSpec&quot;: { # Feature attribution monitoring spec. # Feature attribution monitoring spec.
      &quot;batchExplanationDedicatedResources&quot;: { # A description of resources that are used for performing batch operations, are dedicated to a Model, and need manual configuration. # The config of resources used by the Model Monitoring during the batch explanation for non-AutoML models. If not set, `n1-standard-2` machine type will be used by default.
        &quot;flexStart&quot;: { # FlexStart is used to schedule the deployment workload on DWS resource. It contains the max duration of the deployment. # Optional. Immutable. If set, use DWS resource to schedule the deployment workload. reference: (https://cloud.google.com/blog/products/compute/introducing-dynamic-workload-scheduler)
          &quot;maxRuntimeDuration&quot;: &quot;A String&quot;, # The max duration of the deployment is max_runtime_duration. The deployment will be terminated after the duration. The max_runtime_duration can be set up to 7 days.
        },
        &quot;machineSpec&quot;: { # Specification of a single machine. # Required. Immutable. The specification of a single machine.
          &quot;acceleratorCount&quot;: 42, # The number of accelerators to attach to the machine.
          &quot;acceleratorType&quot;: &quot;A String&quot;, # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
          &quot;gpuPartitionSize&quot;: &quot;A String&quot;, # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size=&quot;1g.10gb&quot;, the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1.
          &quot;machineType&quot;: &quot;A String&quot;, # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
          &quot;multihostGpuNodeCount&quot;: 42, # Optional. Immutable. The number of nodes per replica for multihost GPU deployments.
          &quot;reservationAffinity&quot;: { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
            &quot;key&quot;: &quot;A String&quot;, # Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use `compute.googleapis.com/reservation-name` as the key and specify the name of your reservation as its value.
            &quot;reservationAffinityType&quot;: &quot;A String&quot;, # Required. Specifies the reservation affinity type.
            &quot;values&quot;: [ # Optional. Corresponds to the label values of a reservation resource. This must be the full resource name of the reservation or reservation block.
              &quot;A String&quot;,
            ],
          },
          &quot;tpuTopology&quot;: &quot;A String&quot;, # Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: &quot;2x2x1&quot;).
        },
        &quot;maxReplicaCount&quot;: 42, # Immutable. The maximum number of machine replicas the batch operation may be scaled to. The default value is 10.
        &quot;spot&quot;: True or False, # Optional. If true, schedule the deployment workload on [spot VMs](https://cloud.google.com/kubernetes-engine/docs/concepts/spot-vms).
        &quot;startingReplicaCount&quot;: 42, # Immutable. The number of machine replicas used at the start of the batch operation. If not set, Vertex AI decides starting number, not greater than max_replica_count
      },
      &quot;defaultAlertCondition&quot;: { # Monitoring alert triggered condition. # Default alert condition for all the features.
        &quot;threshold&quot;: 3.14, # A condition that compares a stats value against a threshold. Alert will be triggered if value above the threshold.
      },
      &quot;featureAlertConditions&quot;: { # Per feature alert condition will override default alert condition.
        &quot;a_key&quot;: { # Monitoring alert triggered condition.
          &quot;threshold&quot;: 3.14, # A condition that compares a stats value against a threshold. Alert will be triggered if value above the threshold.
        },
      },
      &quot;features&quot;: [ # Feature names interested in monitoring. These should be a subset of the input feature names specified in the monitoring schema. If the field is not specified all features outlied in the monitoring schema will be used.
        &quot;A String&quot;,
      ],
    },
    &quot;featureDriftSpec&quot;: { # Data drift monitoring spec. Data drift measures the distribution distance between the current dataset and a baseline dataset. A typical use case is to detect data drift between the recent production serving dataset and the training dataset, or to compare the recent production dataset with a dataset from a previous period. # Input feature distribution drift monitoring spec.
      &quot;categoricalMetricType&quot;: &quot;A String&quot;, # Supported metrics type: * l_infinity * jensen_shannon_divergence
      &quot;defaultCategoricalAlertCondition&quot;: { # Monitoring alert triggered condition. # Default alert condition for all the categorical features.
        &quot;threshold&quot;: 3.14, # A condition that compares a stats value against a threshold. Alert will be triggered if value above the threshold.
      },
      &quot;defaultNumericAlertCondition&quot;: { # Monitoring alert triggered condition. # Default alert condition for all the numeric features.
        &quot;threshold&quot;: 3.14, # A condition that compares a stats value against a threshold. Alert will be triggered if value above the threshold.
      },
      &quot;featureAlertConditions&quot;: { # Per feature alert condition will override default alert condition.
        &quot;a_key&quot;: { # Monitoring alert triggered condition.
          &quot;threshold&quot;: 3.14, # A condition that compares a stats value against a threshold. Alert will be triggered if value above the threshold.
        },
      },
      &quot;features&quot;: [ # Feature names / Prediction output names interested in monitoring. These should be a subset of the input feature names or prediction output names specified in the monitoring schema. If the field is not specified all features / prediction outputs outlied in the monitoring schema will be used.
        &quot;A String&quot;,
      ],
      &quot;numericMetricType&quot;: &quot;A String&quot;, # Supported metrics type: * jensen_shannon_divergence
    },
    &quot;predictionOutputDriftSpec&quot;: { # Data drift monitoring spec. Data drift measures the distribution distance between the current dataset and a baseline dataset. A typical use case is to detect data drift between the recent production serving dataset and the training dataset, or to compare the recent production dataset with a dataset from a previous period. # Prediction output distribution drift monitoring spec.
      &quot;categoricalMetricType&quot;: &quot;A String&quot;, # Supported metrics type: * l_infinity * jensen_shannon_divergence
      &quot;defaultCategoricalAlertCondition&quot;: { # Monitoring alert triggered condition. # Default alert condition for all the categorical features.
        &quot;threshold&quot;: 3.14, # A condition that compares a stats value against a threshold. Alert will be triggered if value above the threshold.
      },
      &quot;defaultNumericAlertCondition&quot;: { # Monitoring alert triggered condition. # Default alert condition for all the numeric features.
        &quot;threshold&quot;: 3.14, # A condition that compares a stats value against a threshold. Alert will be triggered if value above the threshold.
      },
      &quot;featureAlertConditions&quot;: { # Per feature alert condition will override default alert condition.
        &quot;a_key&quot;: { # Monitoring alert triggered condition.
          &quot;threshold&quot;: 3.14, # A condition that compares a stats value against a threshold. Alert will be triggered if value above the threshold.
        },
      },
      &quot;features&quot;: [ # Feature names / Prediction output names interested in monitoring. These should be a subset of the input feature names or prediction output names specified in the monitoring schema. If the field is not specified all features / prediction outputs outlied in the monitoring schema will be used.
        &quot;A String&quot;,
      ],
      &quot;numericMetricType&quot;: &quot;A String&quot;, # Supported metrics type: * jensen_shannon_divergence
    },
  },
  &quot;trainingDataset&quot;: { # Model monitoring data input spec. # Optional training dataset used to train the model. It can serve as a reference dataset to identify changes in production.
    &quot;batchPredictionOutput&quot;: { # Data from Vertex AI Batch prediction job output. # Vertex AI Batch prediction Job.
      &quot;batchPredictionJob&quot;: &quot;A String&quot;, # Vertex AI Batch prediction job resource name. The job must match the model version specified in [ModelMonitor].[model_monitoring_target].
    },
    &quot;columnizedDataset&quot;: { # Input dataset spec. # Columnized dataset.
      &quot;bigquerySource&quot;: { # Dataset spec for data sotred in BigQuery. # BigQuery data source.
        &quot;query&quot;: &quot;A String&quot;, # Standard SQL to be used instead of the `table_uri`.
        &quot;tableUri&quot;: &quot;A String&quot;, # BigQuery URI to a table, up to 2000 characters long. All the columns in the table will be selected. Accepted forms: * BigQuery path. For example: `bq://projectId.bqDatasetId.bqTableId`.
      },
      &quot;gcsSource&quot;: { # Dataset spec for data stored in Google Cloud Storage. # Google Cloud Storage data source.
        &quot;format&quot;: &quot;A String&quot;, # Data format of the dataset.
        &quot;gcsUri&quot;: &quot;A String&quot;, # Google Cloud Storage URI to the input file(s). May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/wildcards.
      },
      &quot;timestampField&quot;: &quot;A String&quot;, # The timestamp field. Usually for serving data.
      &quot;vertexDataset&quot;: &quot;A String&quot;, # Resource name of the Vertex AI managed dataset.
    },
    &quot;timeInterval&quot;: { # Represents a time interval, encoded as a Timestamp start (inclusive) and a Timestamp end (exclusive). The start must be less than or equal to the end. When the start equals the end, the interval is empty (matches no time). When both start and end are unspecified, the interval matches any time. # The time interval (pair of start_time and end_time) for which results should be returned.
      &quot;endTime&quot;: &quot;A String&quot;, # Optional. Exclusive end of the interval. If specified, a Timestamp matching this interval will have to be before the end.
      &quot;startTime&quot;: &quot;A String&quot;, # Optional. Inclusive start of the interval. If specified, a Timestamp matching this interval will have to be the same or after the start.
    },
    &quot;timeOffset&quot;: { # Time offset setting. # The time offset setting for which results should be returned.
      &quot;offset&quot;: &quot;A String&quot;, # [offset] is the time difference from the cut-off time. For scheduled jobs, the cut-off time is the scheduled time. For non-scheduled jobs, it&#x27;s the time when the job was created. Currently we support the following format: &#x27;w|W&#x27;: Week, &#x27;d|D&#x27;: Day, &#x27;h|H&#x27;: Hour E.g. &#x27;1h&#x27; stands for 1 hour, &#x27;2d&#x27; stands for 2 days.
      &quot;window&quot;: &quot;A String&quot;, # [window] refers to the scope of data selected for analysis. It allows you to specify the quantity of data you wish to examine. Currently we support the following format: &#x27;w|W&#x27;: Week, &#x27;d|D&#x27;: Day, &#x27;h|H&#x27;: Hour E.g. &#x27;1h&#x27; stands for 1 hour, &#x27;2d&#x27; stands for 2 days.
    },
    &quot;vertexEndpointLogs&quot;: { # Data from Vertex AI Endpoint request response logging. # Vertex AI Endpoint request &amp; response logging.
      &quot;endpoints&quot;: [ # List of endpoint resource names. The endpoints must enable the logging with the [Endpoint].[request_response_logging_config], and must contain the deployed model corresponding to the model version specified in [ModelMonitor].[model_monitoring_target].
        &quot;A String&quot;,
      ],
    },
  },
  &quot;updateTime&quot;: &quot;A String&quot;, # Output only. Timestamp when this ModelMonitor was updated most recently.
}</pre>
</div>

<div class="method">
    <code class="details" id="list">list(parent, filter=None, pageSize=None, pageToken=None, readMask=None, x__xgafv=None)</code>
  <pre>Lists ModelMonitors in a Location.

Args:
  parent: string, Required. The resource name of the Location to list the ModelMonitors from. Format: `projects/{project}/locations/{location}` (required)
  filter: string, The standard list filter. More detail in [AIP-160](https://google.aip.dev/160).
  pageSize: integer, The standard list page size.
  pageToken: string, The standard list page token.
  readMask: string, Mask specifying which fields to read.
  x__xgafv: string, V1 error format.
    Allowed values
      1 - v1 error format
      2 - v2 error format

Returns:
  An object of the form:

    { # Response message for ModelMonitoringService.ListModelMonitors
  &quot;modelMonitors&quot;: [ # List of ModelMonitor in the requested page.
    { # Vertex AI Model Monitoring Service serves as a central hub for the analysis and visualization of data quality and performance related to models. ModelMonitor stands as a top level resource for overseeing your model monitoring tasks.
      &quot;createTime&quot;: &quot;A String&quot;, # Output only. Timestamp when this ModelMonitor was created.
      &quot;displayName&quot;: &quot;A String&quot;, # The display name of the ModelMonitor. The name can be up to 128 characters long and can consist of any UTF-8.
      &quot;encryptionSpec&quot;: { # Represents a customer-managed encryption key spec that can be applied to a top-level resource. # Customer-managed encryption key spec for a ModelMonitor. If set, this ModelMonitor and all sub-resources of this ModelMonitor will be secured by this key.
        &quot;kmsKeyName&quot;: &quot;A String&quot;, # Required. The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: `projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key`. The key needs to be in the same region as where the compute resource is created.
      },
      &quot;explanationSpec&quot;: { # Specification of Model explanation. # Optional model explanation spec. It is used for feature attribution monitoring.
        &quot;metadata&quot;: { # Metadata describing the Model&#x27;s input and output for explanation. # Optional. Metadata describing the Model&#x27;s input and output for explanation.
          &quot;featureAttributionsSchemaUri&quot;: &quot;A String&quot;, # Points to a YAML file stored on Google Cloud Storage describing the format of the feature attributions. The schema is defined as an OpenAPI 3.0.2 [Schema Object](https://github.com/OAI/OpenAPI-Specification/blob/main/versions/3.0.2.md#schemaObject). AutoML tabular Models always have this field populated by Vertex AI. Note: The URI given on output may be different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
          &quot;inputs&quot;: { # Required. Map from feature names to feature input metadata. Keys are the name of the features. Values are the specification of the feature. An empty InputMetadata is valid. It describes a text feature which has the name specified as the key in ExplanationMetadata.inputs. The baseline of the empty feature is chosen by Vertex AI. For Vertex AI-provided Tensorflow images, the key can be any friendly name of the feature. Once specified, featureAttributions are keyed by this key (if not grouped with another feature). For custom images, the key must match with the key in instance.
            &quot;a_key&quot;: { # Metadata of the input of a feature. Fields other than InputMetadata.input_baselines are applicable only for Models that are using Vertex AI-provided images for Tensorflow.
              &quot;denseShapeTensorName&quot;: &quot;A String&quot;, # Specifies the shape of the values of the input if the input is a sparse representation. Refer to Tensorflow documentation for more details: https://www.tensorflow.org/api_docs/python/tf/sparse/SparseTensor.
              &quot;encodedBaselines&quot;: [ # A list of baselines for the encoded tensor. The shape of each baseline should match the shape of the encoded tensor. If a scalar is provided, Vertex AI broadcasts to the same shape as the encoded tensor.
                &quot;&quot;,
              ],
              &quot;encodedTensorName&quot;: &quot;A String&quot;, # Encoded tensor is a transformation of the input tensor. Must be provided if choosing Integrated Gradients attribution or XRAI attribution and the input tensor is not differentiable. An encoded tensor is generated if the input tensor is encoded by a lookup table.
              &quot;encoding&quot;: &quot;A String&quot;, # Defines how the feature is encoded into the input tensor. Defaults to IDENTITY.
              &quot;featureValueDomain&quot;: { # Domain details of the input feature value. Provides numeric information about the feature, such as its range (min, max). If the feature has been pre-processed, for example with z-scoring, then it provides information about how to recover the original feature. For example, if the input feature is an image and it has been pre-processed to obtain 0-mean and stddev = 1 values, then original_mean, and original_stddev refer to the mean and stddev of the original feature (e.g. image tensor) from which input feature (with mean = 0 and stddev = 1) was obtained. # The domain details of the input feature value. Like min/max, original mean or standard deviation if normalized.
                &quot;maxValue&quot;: 3.14, # The maximum permissible value for this feature.
                &quot;minValue&quot;: 3.14, # The minimum permissible value for this feature.
                &quot;originalMean&quot;: 3.14, # If this input feature has been normalized to a mean value of 0, the original_mean specifies the mean value of the domain prior to normalization.
                &quot;originalStddev&quot;: 3.14, # If this input feature has been normalized to a standard deviation of 1.0, the original_stddev specifies the standard deviation of the domain prior to normalization.
              },
              &quot;groupName&quot;: &quot;A String&quot;, # Name of the group that the input belongs to. Features with the same group name will be treated as one feature when computing attributions. Features grouped together can have different shapes in value. If provided, there will be one single attribution generated in Attribution.feature_attributions, keyed by the group name.
              &quot;indexFeatureMapping&quot;: [ # A list of feature names for each index in the input tensor. Required when the input InputMetadata.encoding is BAG_OF_FEATURES, BAG_OF_FEATURES_SPARSE, INDICATOR.
                &quot;A String&quot;,
              ],
              &quot;indicesTensorName&quot;: &quot;A String&quot;, # Specifies the index of the values of the input tensor. Required when the input tensor is a sparse representation. Refer to Tensorflow documentation for more details: https://www.tensorflow.org/api_docs/python/tf/sparse/SparseTensor.
              &quot;inputBaselines&quot;: [ # Baseline inputs for this feature. If no baseline is specified, Vertex AI chooses the baseline for this feature. If multiple baselines are specified, Vertex AI returns the average attributions across them in Attribution.feature_attributions. For Vertex AI-provided Tensorflow images (both 1.x and 2.x), the shape of each baseline must match the shape of the input tensor. If a scalar is provided, we broadcast to the same shape as the input tensor. For custom images, the element of the baselines must be in the same format as the feature&#x27;s input in the instance[]. The schema of any single instance may be specified via Endpoint&#x27;s DeployedModels&#x27; Model&#x27;s PredictSchemata&#x27;s instance_schema_uri.
                &quot;&quot;,
              ],
              &quot;inputTensorName&quot;: &quot;A String&quot;, # Name of the input tensor for this feature. Required and is only applicable to Vertex AI-provided images for Tensorflow.
              &quot;modality&quot;: &quot;A String&quot;, # Modality of the feature. Valid values are: numeric, image. Defaults to numeric.
              &quot;visualization&quot;: { # Visualization configurations for image explanation. # Visualization configurations for image explanation.
                &quot;clipPercentLowerbound&quot;: 3.14, # Excludes attributions below the specified percentile, from the highlighted areas. Defaults to 62.
                &quot;clipPercentUpperbound&quot;: 3.14, # Excludes attributions above the specified percentile from the highlighted areas. Using the clip_percent_upperbound and clip_percent_lowerbound together can be useful for filtering out noise and making it easier to see areas of strong attribution. Defaults to 99.9.
                &quot;colorMap&quot;: &quot;A String&quot;, # The color scheme used for the highlighted areas. Defaults to PINK_GREEN for Integrated Gradients attribution, which shows positive attributions in green and negative in pink. Defaults to VIRIDIS for XRAI attribution, which highlights the most influential regions in yellow and the least influential in blue.
                &quot;overlayType&quot;: &quot;A String&quot;, # How the original image is displayed in the visualization. Adjusting the overlay can help increase visual clarity if the original image makes it difficult to view the visualization. Defaults to NONE.
                &quot;polarity&quot;: &quot;A String&quot;, # Whether to only highlight pixels with positive contributions, negative or both. Defaults to POSITIVE.
                &quot;type&quot;: &quot;A String&quot;, # Type of the image visualization. Only applicable to Integrated Gradients attribution. OUTLINES shows regions of attribution, while PIXELS shows per-pixel attribution. Defaults to OUTLINES.
              },
            },
          },
          &quot;latentSpaceSource&quot;: &quot;A String&quot;, # Name of the source to generate embeddings for example based explanations.
          &quot;outputs&quot;: { # Required. Map from output names to output metadata. For Vertex AI-provided Tensorflow images, keys can be any user defined string that consists of any UTF-8 characters. For custom images, keys are the name of the output field in the prediction to be explained. Currently only one key is allowed.
            &quot;a_key&quot;: { # Metadata of the prediction output to be explained.
              &quot;displayNameMappingKey&quot;: &quot;A String&quot;, # Specify a field name in the prediction to look for the display name. Use this if the prediction contains the display names for the outputs. The display names in the prediction must have the same shape of the outputs, so that it can be located by Attribution.output_index for a specific output.
              &quot;indexDisplayNameMapping&quot;: &quot;&quot;, # Static mapping between the index and display name. Use this if the outputs are a deterministic n-dimensional array, e.g. a list of scores of all the classes in a pre-defined order for a multi-classification Model. It&#x27;s not feasible if the outputs are non-deterministic, e.g. the Model produces top-k classes or sort the outputs by their values. The shape of the value must be an n-dimensional array of strings. The number of dimensions must match that of the outputs to be explained. The Attribution.output_display_name is populated by locating in the mapping with Attribution.output_index.
              &quot;outputTensorName&quot;: &quot;A String&quot;, # Name of the output tensor. Required and is only applicable to Vertex AI provided images for Tensorflow.
            },
          },
        },
        &quot;parameters&quot;: { # Parameters to configure explaining for Model&#x27;s predictions. # Required. Parameters that configure explaining of the Model&#x27;s predictions.
          &quot;examples&quot;: { # Example-based explainability that returns the nearest neighbors from the provided dataset. # Example-based explanations that returns the nearest neighbors from the provided dataset.
            &quot;exampleGcsSource&quot;: { # The Cloud Storage input instances. # The Cloud Storage input instances.
              &quot;dataFormat&quot;: &quot;A String&quot;, # The format in which instances are given, if not specified, assume it&#x27;s JSONL format. Currently only JSONL format is supported.
              &quot;gcsSource&quot;: { # The Google Cloud Storage location for the input content. # The Cloud Storage location for the input instances.
                &quot;uris&quot;: [ # Required. Google Cloud Storage URI(-s) to the input file(s). May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/wildcards.
                  &quot;A String&quot;,
                ],
              },
            },
            &quot;gcsSource&quot;: { # The Google Cloud Storage location for the input content. # The Cloud Storage locations that contain the instances to be indexed for approximate nearest neighbor search.
              &quot;uris&quot;: [ # Required. Google Cloud Storage URI(-s) to the input file(s). May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/wildcards.
                &quot;A String&quot;,
              ],
            },
            &quot;nearestNeighborSearchConfig&quot;: &quot;&quot;, # The full configuration for the generated index, the semantics are the same as metadata and should match [NearestNeighborSearchConfig](https://cloud.google.com/vertex-ai/docs/explainable-ai/configuring-explanations-example-based#nearest-neighbor-search-config).
            &quot;neighborCount&quot;: 42, # The number of neighbors to return when querying for examples.
            &quot;presets&quot;: { # Preset configuration for example-based explanations # Simplified preset configuration, which automatically sets configuration values based on the desired query speed-precision trade-off and modality.
              &quot;modality&quot;: &quot;A String&quot;, # The modality of the uploaded model, which automatically configures the distance measurement and feature normalization for the underlying example index and queries. If your model does not precisely fit one of these types, it is okay to choose the closest type.
              &quot;query&quot;: &quot;A String&quot;, # Preset option controlling parameters for speed-precision trade-off when querying for examples. If omitted, defaults to `PRECISE`.
            },
          },
          &quot;integratedGradientsAttribution&quot;: { # An attribution method that computes the Aumann-Shapley value taking advantage of the model&#x27;s fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1703.01365 # An attribution method that computes Aumann-Shapley values taking advantage of the model&#x27;s fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1703.01365
            &quot;blurBaselineConfig&quot;: { # Config for blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383 # Config for IG with blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383
              &quot;maxBlurSigma&quot;: 3.14, # The standard deviation of the blur kernel for the blurred baseline. The same blurring parameter is used for both the height and the width dimension. If not set, the method defaults to the zero (i.e. black for images) baseline.
            },
            &quot;smoothGradConfig&quot;: { # Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf # Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
              &quot;featureNoiseSigma&quot;: { # Noise sigma by features. Noise sigma represents the standard deviation of the gaussian kernel that will be used to add noise to interpolated inputs prior to computing gradients. # This is similar to noise_sigma, but provides additional flexibility. A separate noise sigma can be provided for each feature, which is useful if their distributions are different. No noise is added to features that are not set. If this field is unset, noise_sigma will be used for all features.
                &quot;noiseSigma&quot;: [ # Noise sigma per feature. No noise is added to features that are not set.
                  { # Noise sigma for a single feature.
                    &quot;name&quot;: &quot;A String&quot;, # The name of the input feature for which noise sigma is provided. The features are defined in explanation metadata inputs.
                    &quot;sigma&quot;: 3.14, # This represents the standard deviation of the Gaussian kernel that will be used to add noise to the feature prior to computing gradients. Similar to noise_sigma but represents the noise added to the current feature. Defaults to 0.1.
                  },
                ],
              },
              &quot;noiseSigma&quot;: 3.14, # This is a single float value and will be used to add noise to all the features. Use this field when all features are normalized to have the same distribution: scale to range [0, 1], [-1, 1] or z-scoring, where features are normalized to have 0-mean and 1-variance. Learn more about [normalization](https://developers.google.com/machine-learning/data-prep/transform/normalization). For best results the recommended value is about 10% - 20% of the standard deviation of the input feature. Refer to section 3.2 of the SmoothGrad paper: https://arxiv.org/pdf/1706.03825.pdf. Defaults to 0.1. If the distribution is different per feature, set feature_noise_sigma instead for each feature.
              &quot;noisySampleCount&quot;: 42, # The number of gradient samples to use for approximation. The higher this number, the more accurate the gradient is, but the runtime complexity increases by this factor as well. Valid range of its value is [1, 50]. Defaults to 3.
            },
            &quot;stepCount&quot;: 42, # Required. The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is within the desired error range. Valid range of its value is [1, 100], inclusively.
          },
          &quot;outputIndices&quot;: [ # If populated, only returns attributions that have output_index contained in output_indices. It must be an ndarray of integers, with the same shape of the output it&#x27;s explaining. If not populated, returns attributions for top_k indices of outputs. If neither top_k nor output_indices is populated, returns the argmax index of the outputs. Only applicable to Models that predict multiple outputs (e,g, multi-class Models that predict multiple classes).
            &quot;&quot;,
          ],
          &quot;sampledShapleyAttribution&quot;: { # An attribution method that approximates Shapley values for features that contribute to the label being predicted. A sampling strategy is used to approximate the value rather than considering all subsets of features. # An attribution method that approximates Shapley values for features that contribute to the label being predicted. A sampling strategy is used to approximate the value rather than considering all subsets of features. Refer to this paper for model details: https://arxiv.org/abs/1306.4265.
            &quot;pathCount&quot;: 42, # Required. The number of feature permutations to consider when approximating the Shapley values. Valid range of its value is [1, 50], inclusively.
          },
          &quot;topK&quot;: 42, # If populated, returns attributions for top K indices of outputs (defaults to 1). Only applies to Models that predicts more than one outputs (e,g, multi-class Models). When set to -1, returns explanations for all outputs.
          &quot;xraiAttribution&quot;: { # An explanation method that redistributes Integrated Gradients attributions to segmented regions, taking advantage of the model&#x27;s fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1906.02825 Supported only by image Models. # An attribution method that redistributes Integrated Gradients attribution to segmented regions, taking advantage of the model&#x27;s fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1906.02825 XRAI currently performs better on natural images, like a picture of a house or an animal. If the images are taken in artificial environments, like a lab or manufacturing line, or from diagnostic equipment, like x-rays or quality-control cameras, use Integrated Gradients instead.
            &quot;blurBaselineConfig&quot;: { # Config for blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383 # Config for XRAI with blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383
              &quot;maxBlurSigma&quot;: 3.14, # The standard deviation of the blur kernel for the blurred baseline. The same blurring parameter is used for both the height and the width dimension. If not set, the method defaults to the zero (i.e. black for images) baseline.
            },
            &quot;smoothGradConfig&quot;: { # Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf # Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
              &quot;featureNoiseSigma&quot;: { # Noise sigma by features. Noise sigma represents the standard deviation of the gaussian kernel that will be used to add noise to interpolated inputs prior to computing gradients. # This is similar to noise_sigma, but provides additional flexibility. A separate noise sigma can be provided for each feature, which is useful if their distributions are different. No noise is added to features that are not set. If this field is unset, noise_sigma will be used for all features.
                &quot;noiseSigma&quot;: [ # Noise sigma per feature. No noise is added to features that are not set.
                  { # Noise sigma for a single feature.
                    &quot;name&quot;: &quot;A String&quot;, # The name of the input feature for which noise sigma is provided. The features are defined in explanation metadata inputs.
                    &quot;sigma&quot;: 3.14, # This represents the standard deviation of the Gaussian kernel that will be used to add noise to the feature prior to computing gradients. Similar to noise_sigma but represents the noise added to the current feature. Defaults to 0.1.
                  },
                ],
              },
              &quot;noiseSigma&quot;: 3.14, # This is a single float value and will be used to add noise to all the features. Use this field when all features are normalized to have the same distribution: scale to range [0, 1], [-1, 1] or z-scoring, where features are normalized to have 0-mean and 1-variance. Learn more about [normalization](https://developers.google.com/machine-learning/data-prep/transform/normalization). For best results the recommended value is about 10% - 20% of the standard deviation of the input feature. Refer to section 3.2 of the SmoothGrad paper: https://arxiv.org/pdf/1706.03825.pdf. Defaults to 0.1. If the distribution is different per feature, set feature_noise_sigma instead for each feature.
              &quot;noisySampleCount&quot;: 42, # The number of gradient samples to use for approximation. The higher this number, the more accurate the gradient is, but the runtime complexity increases by this factor as well. Valid range of its value is [1, 50]. Defaults to 3.
            },
            &quot;stepCount&quot;: 42, # Required. The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is met within the desired error range. Valid range of its value is [1, 100], inclusively.
          },
        },
      },
      &quot;modelMonitoringSchema&quot;: { # The Model Monitoring Schema definition. # Monitoring Schema is to specify the model&#x27;s features, prediction outputs and ground truth properties. It is used to extract pertinent data from the dataset and to process features based on their properties. Make sure that the schema aligns with your dataset, if it does not, we will be unable to extract data from the dataset. It is required for most models, but optional for Vertex AI AutoML Tables unless the schem information is not available.
        &quot;featureFields&quot;: [ # Feature names of the model. Vertex AI will try to match the features from your dataset as follows: * For &#x27;csv&#x27; files, the header names are required, and we will extract the corresponding feature values when the header names align with the feature names. * For &#x27;jsonl&#x27; files, we will extract the corresponding feature values if the key names match the feature names. Note: Nested features are not supported, so please ensure your features are flattened. Ensure the feature values are scalar or an array of scalars. * For &#x27;bigquery&#x27; dataset, we will extract the corresponding feature values if the column names match the feature names. Note: The column type can be a scalar or an array of scalars. STRUCT or JSON types are not supported. You may use SQL queries to select or aggregate the relevant features from your original table. However, ensure that the &#x27;schema&#x27; of the query results meets our requirements. * For the Vertex AI Endpoint Request Response Logging table or Vertex AI Batch Prediction Job results. If the instance_type is an array, ensure that the sequence in feature_fields matches the order of features in the prediction instance. We will match the feature with the array in the order specified in [feature_fields].
          { # Schema field definition.
            &quot;dataType&quot;: &quot;A String&quot;, # Supported data types are: `float` `integer` `boolean` `string` `categorical`
            &quot;name&quot;: &quot;A String&quot;, # Field name.
            &quot;repeated&quot;: True or False, # Describes if the schema field is an array of given data type.
          },
        ],
        &quot;groundTruthFields&quot;: [ # Target /ground truth names of the model.
          { # Schema field definition.
            &quot;dataType&quot;: &quot;A String&quot;, # Supported data types are: `float` `integer` `boolean` `string` `categorical`
            &quot;name&quot;: &quot;A String&quot;, # Field name.
            &quot;repeated&quot;: True or False, # Describes if the schema field is an array of given data type.
          },
        ],
        &quot;predictionFields&quot;: [ # Prediction output names of the model. The requirements are the same as the feature_fields. For AutoML Tables, the prediction output name presented in schema will be: `predicted_{target_column}`, the `target_column` is the one you specified when you train the model. For Prediction output drift analysis: * AutoML Classification, the distribution of the argmax label will be analyzed. * AutoML Regression, the distribution of the value will be analyzed.
          { # Schema field definition.
            &quot;dataType&quot;: &quot;A String&quot;, # Supported data types are: `float` `integer` `boolean` `string` `categorical`
            &quot;name&quot;: &quot;A String&quot;, # Field name.
            &quot;repeated&quot;: True or False, # Describes if the schema field is an array of given data type.
          },
        ],
      },
      &quot;modelMonitoringTarget&quot;: { # The monitoring target refers to the entity that is subject to analysis. e.g. Vertex AI Model version. # The entity that is subject to analysis. Currently only models in Vertex AI Model Registry are supported. If you want to analyze the model which is outside the Vertex AI, you could register a model in Vertex AI Model Registry using just a display name.
        &quot;vertexModel&quot;: { # Model in Vertex AI Model Registry. # Model in Vertex AI Model Registry.
          &quot;model&quot;: &quot;A String&quot;, # Model resource name. Format: projects/{project}/locations/{location}/models/{model}.
          &quot;modelVersionId&quot;: &quot;A String&quot;, # Model version id.
        },
      },
      &quot;name&quot;: &quot;A String&quot;, # Immutable. Resource name of the ModelMonitor. Format: `projects/{project}/locations/{location}/modelMonitors/{model_monitor}`.
      &quot;notificationSpec&quot;: { # Notification spec(email, notification channel) for model monitoring statistics/alerts. # Optional default notification spec, it can be overridden in the ModelMonitoringJob notification spec.
        &quot;emailConfig&quot;: { # The config for email alerts. # Email alert config.
          &quot;userEmails&quot;: [ # The email addresses to send the alerts.
            &quot;A String&quot;,
          ],
        },
        &quot;enableCloudLogging&quot;: True or False, # Dump the anomalies to Cloud Logging. The anomalies will be put to json payload encoded from proto google.cloud.aiplatform.logging.ModelMonitoringAnomaliesLogEntry. This can be further sinked to Pub/Sub or any other services supported by Cloud Logging.
        &quot;notificationChannelConfigs&quot;: [ # Notification channel config.
          { # Google Cloud Notification Channel config.
            &quot;notificationChannel&quot;: &quot;A String&quot;, # Resource names of the NotificationChannels. Must be of the format `projects//notificationChannels/`
          },
        ],
      },
      &quot;outputSpec&quot;: { # Specification for the export destination of monitoring results, including metrics, logs, etc. # Optional default monitoring metrics/logs export spec, it can be overridden in the ModelMonitoringJob output spec. If not specified, a default Google Cloud Storage bucket will be created under your project.
        &quot;gcsBaseDirectory&quot;: { # The Google Cloud Storage location where the output is to be written to. # Google Cloud Storage base folder path for metrics, error logs, etc.
          &quot;outputUriPrefix&quot;: &quot;A String&quot;, # Required. Google Cloud Storage URI to output directory. If the uri doesn&#x27;t end with &#x27;/&#x27;, a &#x27;/&#x27; will be automatically appended. The directory is created if it doesn&#x27;t exist.
        },
      },
      &quot;satisfiesPzi&quot;: True or False, # Output only. Reserved for future use.
      &quot;satisfiesPzs&quot;: True or False, # Output only. Reserved for future use.
      &quot;tabularObjective&quot;: { # Tabular monitoring objective. # Optional default tabular model monitoring objective.
        &quot;featureAttributionSpec&quot;: { # Feature attribution monitoring spec. # Feature attribution monitoring spec.
          &quot;batchExplanationDedicatedResources&quot;: { # A description of resources that are used for performing batch operations, are dedicated to a Model, and need manual configuration. # The config of resources used by the Model Monitoring during the batch explanation for non-AutoML models. If not set, `n1-standard-2` machine type will be used by default.
            &quot;flexStart&quot;: { # FlexStart is used to schedule the deployment workload on DWS resource. It contains the max duration of the deployment. # Optional. Immutable. If set, use DWS resource to schedule the deployment workload. reference: (https://cloud.google.com/blog/products/compute/introducing-dynamic-workload-scheduler)
              &quot;maxRuntimeDuration&quot;: &quot;A String&quot;, # The max duration of the deployment is max_runtime_duration. The deployment will be terminated after the duration. The max_runtime_duration can be set up to 7 days.
            },
            &quot;machineSpec&quot;: { # Specification of a single machine. # Required. Immutable. The specification of a single machine.
              &quot;acceleratorCount&quot;: 42, # The number of accelerators to attach to the machine.
              &quot;acceleratorType&quot;: &quot;A String&quot;, # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
              &quot;gpuPartitionSize&quot;: &quot;A String&quot;, # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size=&quot;1g.10gb&quot;, the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1.
              &quot;machineType&quot;: &quot;A String&quot;, # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
              &quot;multihostGpuNodeCount&quot;: 42, # Optional. Immutable. The number of nodes per replica for multihost GPU deployments.
              &quot;reservationAffinity&quot;: { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
                &quot;key&quot;: &quot;A String&quot;, # Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use `compute.googleapis.com/reservation-name` as the key and specify the name of your reservation as its value.
                &quot;reservationAffinityType&quot;: &quot;A String&quot;, # Required. Specifies the reservation affinity type.
                &quot;values&quot;: [ # Optional. Corresponds to the label values of a reservation resource. This must be the full resource name of the reservation or reservation block.
                  &quot;A String&quot;,
                ],
              },
              &quot;tpuTopology&quot;: &quot;A String&quot;, # Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: &quot;2x2x1&quot;).
            },
            &quot;maxReplicaCount&quot;: 42, # Immutable. The maximum number of machine replicas the batch operation may be scaled to. The default value is 10.
            &quot;spot&quot;: True or False, # Optional. If true, schedule the deployment workload on [spot VMs](https://cloud.google.com/kubernetes-engine/docs/concepts/spot-vms).
            &quot;startingReplicaCount&quot;: 42, # Immutable. The number of machine replicas used at the start of the batch operation. If not set, Vertex AI decides starting number, not greater than max_replica_count
          },
          &quot;defaultAlertCondition&quot;: { # Monitoring alert triggered condition. # Default alert condition for all the features.
            &quot;threshold&quot;: 3.14, # A condition that compares a stats value against a threshold. Alert will be triggered if value above the threshold.
          },
          &quot;featureAlertConditions&quot;: { # Per feature alert condition will override default alert condition.
            &quot;a_key&quot;: { # Monitoring alert triggered condition.
              &quot;threshold&quot;: 3.14, # A condition that compares a stats value against a threshold. Alert will be triggered if value above the threshold.
            },
          },
          &quot;features&quot;: [ # Feature names interested in monitoring. These should be a subset of the input feature names specified in the monitoring schema. If the field is not specified all features outlied in the monitoring schema will be used.
            &quot;A String&quot;,
          ],
        },
        &quot;featureDriftSpec&quot;: { # Data drift monitoring spec. Data drift measures the distribution distance between the current dataset and a baseline dataset. A typical use case is to detect data drift between the recent production serving dataset and the training dataset, or to compare the recent production dataset with a dataset from a previous period. # Input feature distribution drift monitoring spec.
          &quot;categoricalMetricType&quot;: &quot;A String&quot;, # Supported metrics type: * l_infinity * jensen_shannon_divergence
          &quot;defaultCategoricalAlertCondition&quot;: { # Monitoring alert triggered condition. # Default alert condition for all the categorical features.
            &quot;threshold&quot;: 3.14, # A condition that compares a stats value against a threshold. Alert will be triggered if value above the threshold.
          },
          &quot;defaultNumericAlertCondition&quot;: { # Monitoring alert triggered condition. # Default alert condition for all the numeric features.
            &quot;threshold&quot;: 3.14, # A condition that compares a stats value against a threshold. Alert will be triggered if value above the threshold.
          },
          &quot;featureAlertConditions&quot;: { # Per feature alert condition will override default alert condition.
            &quot;a_key&quot;: { # Monitoring alert triggered condition.
              &quot;threshold&quot;: 3.14, # A condition that compares a stats value against a threshold. Alert will be triggered if value above the threshold.
            },
          },
          &quot;features&quot;: [ # Feature names / Prediction output names interested in monitoring. These should be a subset of the input feature names or prediction output names specified in the monitoring schema. If the field is not specified all features / prediction outputs outlied in the monitoring schema will be used.
            &quot;A String&quot;,
          ],
          &quot;numericMetricType&quot;: &quot;A String&quot;, # Supported metrics type: * jensen_shannon_divergence
        },
        &quot;predictionOutputDriftSpec&quot;: { # Data drift monitoring spec. Data drift measures the distribution distance between the current dataset and a baseline dataset. A typical use case is to detect data drift between the recent production serving dataset and the training dataset, or to compare the recent production dataset with a dataset from a previous period. # Prediction output distribution drift monitoring spec.
          &quot;categoricalMetricType&quot;: &quot;A String&quot;, # Supported metrics type: * l_infinity * jensen_shannon_divergence
          &quot;defaultCategoricalAlertCondition&quot;: { # Monitoring alert triggered condition. # Default alert condition for all the categorical features.
            &quot;threshold&quot;: 3.14, # A condition that compares a stats value against a threshold. Alert will be triggered if value above the threshold.
          },
          &quot;defaultNumericAlertCondition&quot;: { # Monitoring alert triggered condition. # Default alert condition for all the numeric features.
            &quot;threshold&quot;: 3.14, # A condition that compares a stats value against a threshold. Alert will be triggered if value above the threshold.
          },
          &quot;featureAlertConditions&quot;: { # Per feature alert condition will override default alert condition.
            &quot;a_key&quot;: { # Monitoring alert triggered condition.
              &quot;threshold&quot;: 3.14, # A condition that compares a stats value against a threshold. Alert will be triggered if value above the threshold.
            },
          },
          &quot;features&quot;: [ # Feature names / Prediction output names interested in monitoring. These should be a subset of the input feature names or prediction output names specified in the monitoring schema. If the field is not specified all features / prediction outputs outlied in the monitoring schema will be used.
            &quot;A String&quot;,
          ],
          &quot;numericMetricType&quot;: &quot;A String&quot;, # Supported metrics type: * jensen_shannon_divergence
        },
      },
      &quot;trainingDataset&quot;: { # Model monitoring data input spec. # Optional training dataset used to train the model. It can serve as a reference dataset to identify changes in production.
        &quot;batchPredictionOutput&quot;: { # Data from Vertex AI Batch prediction job output. # Vertex AI Batch prediction Job.
          &quot;batchPredictionJob&quot;: &quot;A String&quot;, # Vertex AI Batch prediction job resource name. The job must match the model version specified in [ModelMonitor].[model_monitoring_target].
        },
        &quot;columnizedDataset&quot;: { # Input dataset spec. # Columnized dataset.
          &quot;bigquerySource&quot;: { # Dataset spec for data sotred in BigQuery. # BigQuery data source.
            &quot;query&quot;: &quot;A String&quot;, # Standard SQL to be used instead of the `table_uri`.
            &quot;tableUri&quot;: &quot;A String&quot;, # BigQuery URI to a table, up to 2000 characters long. All the columns in the table will be selected. Accepted forms: * BigQuery path. For example: `bq://projectId.bqDatasetId.bqTableId`.
          },
          &quot;gcsSource&quot;: { # Dataset spec for data stored in Google Cloud Storage. # Google Cloud Storage data source.
            &quot;format&quot;: &quot;A String&quot;, # Data format of the dataset.
            &quot;gcsUri&quot;: &quot;A String&quot;, # Google Cloud Storage URI to the input file(s). May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/wildcards.
          },
          &quot;timestampField&quot;: &quot;A String&quot;, # The timestamp field. Usually for serving data.
          &quot;vertexDataset&quot;: &quot;A String&quot;, # Resource name of the Vertex AI managed dataset.
        },
        &quot;timeInterval&quot;: { # Represents a time interval, encoded as a Timestamp start (inclusive) and a Timestamp end (exclusive). The start must be less than or equal to the end. When the start equals the end, the interval is empty (matches no time). When both start and end are unspecified, the interval matches any time. # The time interval (pair of start_time and end_time) for which results should be returned.
          &quot;endTime&quot;: &quot;A String&quot;, # Optional. Exclusive end of the interval. If specified, a Timestamp matching this interval will have to be before the end.
          &quot;startTime&quot;: &quot;A String&quot;, # Optional. Inclusive start of the interval. If specified, a Timestamp matching this interval will have to be the same or after the start.
        },
        &quot;timeOffset&quot;: { # Time offset setting. # The time offset setting for which results should be returned.
          &quot;offset&quot;: &quot;A String&quot;, # [offset] is the time difference from the cut-off time. For scheduled jobs, the cut-off time is the scheduled time. For non-scheduled jobs, it&#x27;s the time when the job was created. Currently we support the following format: &#x27;w|W&#x27;: Week, &#x27;d|D&#x27;: Day, &#x27;h|H&#x27;: Hour E.g. &#x27;1h&#x27; stands for 1 hour, &#x27;2d&#x27; stands for 2 days.
          &quot;window&quot;: &quot;A String&quot;, # [window] refers to the scope of data selected for analysis. It allows you to specify the quantity of data you wish to examine. Currently we support the following format: &#x27;w|W&#x27;: Week, &#x27;d|D&#x27;: Day, &#x27;h|H&#x27;: Hour E.g. &#x27;1h&#x27; stands for 1 hour, &#x27;2d&#x27; stands for 2 days.
        },
        &quot;vertexEndpointLogs&quot;: { # Data from Vertex AI Endpoint request response logging. # Vertex AI Endpoint request &amp; response logging.
          &quot;endpoints&quot;: [ # List of endpoint resource names. The endpoints must enable the logging with the [Endpoint].[request_response_logging_config], and must contain the deployed model corresponding to the model version specified in [ModelMonitor].[model_monitoring_target].
            &quot;A String&quot;,
          ],
        },
      },
      &quot;updateTime&quot;: &quot;A String&quot;, # Output only. Timestamp when this ModelMonitor was updated most recently.
    },
  ],
  &quot;nextPageToken&quot;: &quot;A String&quot;, # A token to retrieve the next page of results. Pass to ListModelMonitorsRequest.page_token to obtain that page.
}</pre>
</div>

<div class="method">
    <code class="details" id="list_next">list_next()</code>
  <pre>Retrieves the next page of results.

        Args:
          previous_request: The request for the previous page. (required)
          previous_response: The response from the request for the previous page. (required)

        Returns:
          A request object that you can call &#x27;execute()&#x27; on to request the next
          page. Returns None if there are no more items in the collection.
        </pre>
</div>

<div class="method">
    <code class="details" id="patch">patch(name, body=None, updateMask=None, x__xgafv=None)</code>
  <pre>Updates a ModelMonitor.

Args:
  name: string, Immutable. Resource name of the ModelMonitor. Format: `projects/{project}/locations/{location}/modelMonitors/{model_monitor}`. (required)
  body: object, The request body.
    The object takes the form of:

{ # Vertex AI Model Monitoring Service serves as a central hub for the analysis and visualization of data quality and performance related to models. ModelMonitor stands as a top level resource for overseeing your model monitoring tasks.
  &quot;createTime&quot;: &quot;A String&quot;, # Output only. Timestamp when this ModelMonitor was created.
  &quot;displayName&quot;: &quot;A String&quot;, # The display name of the ModelMonitor. The name can be up to 128 characters long and can consist of any UTF-8.
  &quot;encryptionSpec&quot;: { # Represents a customer-managed encryption key spec that can be applied to a top-level resource. # Customer-managed encryption key spec for a ModelMonitor. If set, this ModelMonitor and all sub-resources of this ModelMonitor will be secured by this key.
    &quot;kmsKeyName&quot;: &quot;A String&quot;, # Required. The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: `projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key`. The key needs to be in the same region as where the compute resource is created.
  },
  &quot;explanationSpec&quot;: { # Specification of Model explanation. # Optional model explanation spec. It is used for feature attribution monitoring.
    &quot;metadata&quot;: { # Metadata describing the Model&#x27;s input and output for explanation. # Optional. Metadata describing the Model&#x27;s input and output for explanation.
      &quot;featureAttributionsSchemaUri&quot;: &quot;A String&quot;, # Points to a YAML file stored on Google Cloud Storage describing the format of the feature attributions. The schema is defined as an OpenAPI 3.0.2 [Schema Object](https://github.com/OAI/OpenAPI-Specification/blob/main/versions/3.0.2.md#schemaObject). AutoML tabular Models always have this field populated by Vertex AI. Note: The URI given on output may be different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
      &quot;inputs&quot;: { # Required. Map from feature names to feature input metadata. Keys are the name of the features. Values are the specification of the feature. An empty InputMetadata is valid. It describes a text feature which has the name specified as the key in ExplanationMetadata.inputs. The baseline of the empty feature is chosen by Vertex AI. For Vertex AI-provided Tensorflow images, the key can be any friendly name of the feature. Once specified, featureAttributions are keyed by this key (if not grouped with another feature). For custom images, the key must match with the key in instance.
        &quot;a_key&quot;: { # Metadata of the input of a feature. Fields other than InputMetadata.input_baselines are applicable only for Models that are using Vertex AI-provided images for Tensorflow.
          &quot;denseShapeTensorName&quot;: &quot;A String&quot;, # Specifies the shape of the values of the input if the input is a sparse representation. Refer to Tensorflow documentation for more details: https://www.tensorflow.org/api_docs/python/tf/sparse/SparseTensor.
          &quot;encodedBaselines&quot;: [ # A list of baselines for the encoded tensor. The shape of each baseline should match the shape of the encoded tensor. If a scalar is provided, Vertex AI broadcasts to the same shape as the encoded tensor.
            &quot;&quot;,
          ],
          &quot;encodedTensorName&quot;: &quot;A String&quot;, # Encoded tensor is a transformation of the input tensor. Must be provided if choosing Integrated Gradients attribution or XRAI attribution and the input tensor is not differentiable. An encoded tensor is generated if the input tensor is encoded by a lookup table.
          &quot;encoding&quot;: &quot;A String&quot;, # Defines how the feature is encoded into the input tensor. Defaults to IDENTITY.
          &quot;featureValueDomain&quot;: { # Domain details of the input feature value. Provides numeric information about the feature, such as its range (min, max). If the feature has been pre-processed, for example with z-scoring, then it provides information about how to recover the original feature. For example, if the input feature is an image and it has been pre-processed to obtain 0-mean and stddev = 1 values, then original_mean, and original_stddev refer to the mean and stddev of the original feature (e.g. image tensor) from which input feature (with mean = 0 and stddev = 1) was obtained. # The domain details of the input feature value. Like min/max, original mean or standard deviation if normalized.
            &quot;maxValue&quot;: 3.14, # The maximum permissible value for this feature.
            &quot;minValue&quot;: 3.14, # The minimum permissible value for this feature.
            &quot;originalMean&quot;: 3.14, # If this input feature has been normalized to a mean value of 0, the original_mean specifies the mean value of the domain prior to normalization.
            &quot;originalStddev&quot;: 3.14, # If this input feature has been normalized to a standard deviation of 1.0, the original_stddev specifies the standard deviation of the domain prior to normalization.
          },
          &quot;groupName&quot;: &quot;A String&quot;, # Name of the group that the input belongs to. Features with the same group name will be treated as one feature when computing attributions. Features grouped together can have different shapes in value. If provided, there will be one single attribution generated in Attribution.feature_attributions, keyed by the group name.
          &quot;indexFeatureMapping&quot;: [ # A list of feature names for each index in the input tensor. Required when the input InputMetadata.encoding is BAG_OF_FEATURES, BAG_OF_FEATURES_SPARSE, INDICATOR.
            &quot;A String&quot;,
          ],
          &quot;indicesTensorName&quot;: &quot;A String&quot;, # Specifies the index of the values of the input tensor. Required when the input tensor is a sparse representation. Refer to Tensorflow documentation for more details: https://www.tensorflow.org/api_docs/python/tf/sparse/SparseTensor.
          &quot;inputBaselines&quot;: [ # Baseline inputs for this feature. If no baseline is specified, Vertex AI chooses the baseline for this feature. If multiple baselines are specified, Vertex AI returns the average attributions across them in Attribution.feature_attributions. For Vertex AI-provided Tensorflow images (both 1.x and 2.x), the shape of each baseline must match the shape of the input tensor. If a scalar is provided, we broadcast to the same shape as the input tensor. For custom images, the element of the baselines must be in the same format as the feature&#x27;s input in the instance[]. The schema of any single instance may be specified via Endpoint&#x27;s DeployedModels&#x27; Model&#x27;s PredictSchemata&#x27;s instance_schema_uri.
            &quot;&quot;,
          ],
          &quot;inputTensorName&quot;: &quot;A String&quot;, # Name of the input tensor for this feature. Required and is only applicable to Vertex AI-provided images for Tensorflow.
          &quot;modality&quot;: &quot;A String&quot;, # Modality of the feature. Valid values are: numeric, image. Defaults to numeric.
          &quot;visualization&quot;: { # Visualization configurations for image explanation. # Visualization configurations for image explanation.
            &quot;clipPercentLowerbound&quot;: 3.14, # Excludes attributions below the specified percentile, from the highlighted areas. Defaults to 62.
            &quot;clipPercentUpperbound&quot;: 3.14, # Excludes attributions above the specified percentile from the highlighted areas. Using the clip_percent_upperbound and clip_percent_lowerbound together can be useful for filtering out noise and making it easier to see areas of strong attribution. Defaults to 99.9.
            &quot;colorMap&quot;: &quot;A String&quot;, # The color scheme used for the highlighted areas. Defaults to PINK_GREEN for Integrated Gradients attribution, which shows positive attributions in green and negative in pink. Defaults to VIRIDIS for XRAI attribution, which highlights the most influential regions in yellow and the least influential in blue.
            &quot;overlayType&quot;: &quot;A String&quot;, # How the original image is displayed in the visualization. Adjusting the overlay can help increase visual clarity if the original image makes it difficult to view the visualization. Defaults to NONE.
            &quot;polarity&quot;: &quot;A String&quot;, # Whether to only highlight pixels with positive contributions, negative or both. Defaults to POSITIVE.
            &quot;type&quot;: &quot;A String&quot;, # Type of the image visualization. Only applicable to Integrated Gradients attribution. OUTLINES shows regions of attribution, while PIXELS shows per-pixel attribution. Defaults to OUTLINES.
          },
        },
      },
      &quot;latentSpaceSource&quot;: &quot;A String&quot;, # Name of the source to generate embeddings for example based explanations.
      &quot;outputs&quot;: { # Required. Map from output names to output metadata. For Vertex AI-provided Tensorflow images, keys can be any user defined string that consists of any UTF-8 characters. For custom images, keys are the name of the output field in the prediction to be explained. Currently only one key is allowed.
        &quot;a_key&quot;: { # Metadata of the prediction output to be explained.
          &quot;displayNameMappingKey&quot;: &quot;A String&quot;, # Specify a field name in the prediction to look for the display name. Use this if the prediction contains the display names for the outputs. The display names in the prediction must have the same shape of the outputs, so that it can be located by Attribution.output_index for a specific output.
          &quot;indexDisplayNameMapping&quot;: &quot;&quot;, # Static mapping between the index and display name. Use this if the outputs are a deterministic n-dimensional array, e.g. a list of scores of all the classes in a pre-defined order for a multi-classification Model. It&#x27;s not feasible if the outputs are non-deterministic, e.g. the Model produces top-k classes or sort the outputs by their values. The shape of the value must be an n-dimensional array of strings. The number of dimensions must match that of the outputs to be explained. The Attribution.output_display_name is populated by locating in the mapping with Attribution.output_index.
          &quot;outputTensorName&quot;: &quot;A String&quot;, # Name of the output tensor. Required and is only applicable to Vertex AI provided images for Tensorflow.
        },
      },
    },
    &quot;parameters&quot;: { # Parameters to configure explaining for Model&#x27;s predictions. # Required. Parameters that configure explaining of the Model&#x27;s predictions.
      &quot;examples&quot;: { # Example-based explainability that returns the nearest neighbors from the provided dataset. # Example-based explanations that returns the nearest neighbors from the provided dataset.
        &quot;exampleGcsSource&quot;: { # The Cloud Storage input instances. # The Cloud Storage input instances.
          &quot;dataFormat&quot;: &quot;A String&quot;, # The format in which instances are given, if not specified, assume it&#x27;s JSONL format. Currently only JSONL format is supported.
          &quot;gcsSource&quot;: { # The Google Cloud Storage location for the input content. # The Cloud Storage location for the input instances.
            &quot;uris&quot;: [ # Required. Google Cloud Storage URI(-s) to the input file(s). May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/wildcards.
              &quot;A String&quot;,
            ],
          },
        },
        &quot;gcsSource&quot;: { # The Google Cloud Storage location for the input content. # The Cloud Storage locations that contain the instances to be indexed for approximate nearest neighbor search.
          &quot;uris&quot;: [ # Required. Google Cloud Storage URI(-s) to the input file(s). May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/wildcards.
            &quot;A String&quot;,
          ],
        },
        &quot;nearestNeighborSearchConfig&quot;: &quot;&quot;, # The full configuration for the generated index, the semantics are the same as metadata and should match [NearestNeighborSearchConfig](https://cloud.google.com/vertex-ai/docs/explainable-ai/configuring-explanations-example-based#nearest-neighbor-search-config).
        &quot;neighborCount&quot;: 42, # The number of neighbors to return when querying for examples.
        &quot;presets&quot;: { # Preset configuration for example-based explanations # Simplified preset configuration, which automatically sets configuration values based on the desired query speed-precision trade-off and modality.
          &quot;modality&quot;: &quot;A String&quot;, # The modality of the uploaded model, which automatically configures the distance measurement and feature normalization for the underlying example index and queries. If your model does not precisely fit one of these types, it is okay to choose the closest type.
          &quot;query&quot;: &quot;A String&quot;, # Preset option controlling parameters for speed-precision trade-off when querying for examples. If omitted, defaults to `PRECISE`.
        },
      },
      &quot;integratedGradientsAttribution&quot;: { # An attribution method that computes the Aumann-Shapley value taking advantage of the model&#x27;s fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1703.01365 # An attribution method that computes Aumann-Shapley values taking advantage of the model&#x27;s fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1703.01365
        &quot;blurBaselineConfig&quot;: { # Config for blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383 # Config for IG with blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383
          &quot;maxBlurSigma&quot;: 3.14, # The standard deviation of the blur kernel for the blurred baseline. The same blurring parameter is used for both the height and the width dimension. If not set, the method defaults to the zero (i.e. black for images) baseline.
        },
        &quot;smoothGradConfig&quot;: { # Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf # Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
          &quot;featureNoiseSigma&quot;: { # Noise sigma by features. Noise sigma represents the standard deviation of the gaussian kernel that will be used to add noise to interpolated inputs prior to computing gradients. # This is similar to noise_sigma, but provides additional flexibility. A separate noise sigma can be provided for each feature, which is useful if their distributions are different. No noise is added to features that are not set. If this field is unset, noise_sigma will be used for all features.
            &quot;noiseSigma&quot;: [ # Noise sigma per feature. No noise is added to features that are not set.
              { # Noise sigma for a single feature.
                &quot;name&quot;: &quot;A String&quot;, # The name of the input feature for which noise sigma is provided. The features are defined in explanation metadata inputs.
                &quot;sigma&quot;: 3.14, # This represents the standard deviation of the Gaussian kernel that will be used to add noise to the feature prior to computing gradients. Similar to noise_sigma but represents the noise added to the current feature. Defaults to 0.1.
              },
            ],
          },
          &quot;noiseSigma&quot;: 3.14, # This is a single float value and will be used to add noise to all the features. Use this field when all features are normalized to have the same distribution: scale to range [0, 1], [-1, 1] or z-scoring, where features are normalized to have 0-mean and 1-variance. Learn more about [normalization](https://developers.google.com/machine-learning/data-prep/transform/normalization). For best results the recommended value is about 10% - 20% of the standard deviation of the input feature. Refer to section 3.2 of the SmoothGrad paper: https://arxiv.org/pdf/1706.03825.pdf. Defaults to 0.1. If the distribution is different per feature, set feature_noise_sigma instead for each feature.
          &quot;noisySampleCount&quot;: 42, # The number of gradient samples to use for approximation. The higher this number, the more accurate the gradient is, but the runtime complexity increases by this factor as well. Valid range of its value is [1, 50]. Defaults to 3.
        },
        &quot;stepCount&quot;: 42, # Required. The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is within the desired error range. Valid range of its value is [1, 100], inclusively.
      },
      &quot;outputIndices&quot;: [ # If populated, only returns attributions that have output_index contained in output_indices. It must be an ndarray of integers, with the same shape of the output it&#x27;s explaining. If not populated, returns attributions for top_k indices of outputs. If neither top_k nor output_indices is populated, returns the argmax index of the outputs. Only applicable to Models that predict multiple outputs (e,g, multi-class Models that predict multiple classes).
        &quot;&quot;,
      ],
      &quot;sampledShapleyAttribution&quot;: { # An attribution method that approximates Shapley values for features that contribute to the label being predicted. A sampling strategy is used to approximate the value rather than considering all subsets of features. # An attribution method that approximates Shapley values for features that contribute to the label being predicted. A sampling strategy is used to approximate the value rather than considering all subsets of features. Refer to this paper for model details: https://arxiv.org/abs/1306.4265.
        &quot;pathCount&quot;: 42, # Required. The number of feature permutations to consider when approximating the Shapley values. Valid range of its value is [1, 50], inclusively.
      },
      &quot;topK&quot;: 42, # If populated, returns attributions for top K indices of outputs (defaults to 1). Only applies to Models that predicts more than one outputs (e,g, multi-class Models). When set to -1, returns explanations for all outputs.
      &quot;xraiAttribution&quot;: { # An explanation method that redistributes Integrated Gradients attributions to segmented regions, taking advantage of the model&#x27;s fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1906.02825 Supported only by image Models. # An attribution method that redistributes Integrated Gradients attribution to segmented regions, taking advantage of the model&#x27;s fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1906.02825 XRAI currently performs better on natural images, like a picture of a house or an animal. If the images are taken in artificial environments, like a lab or manufacturing line, or from diagnostic equipment, like x-rays or quality-control cameras, use Integrated Gradients instead.
        &quot;blurBaselineConfig&quot;: { # Config for blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383 # Config for XRAI with blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383
          &quot;maxBlurSigma&quot;: 3.14, # The standard deviation of the blur kernel for the blurred baseline. The same blurring parameter is used for both the height and the width dimension. If not set, the method defaults to the zero (i.e. black for images) baseline.
        },
        &quot;smoothGradConfig&quot;: { # Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf # Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
          &quot;featureNoiseSigma&quot;: { # Noise sigma by features. Noise sigma represents the standard deviation of the gaussian kernel that will be used to add noise to interpolated inputs prior to computing gradients. # This is similar to noise_sigma, but provides additional flexibility. A separate noise sigma can be provided for each feature, which is useful if their distributions are different. No noise is added to features that are not set. If this field is unset, noise_sigma will be used for all features.
            &quot;noiseSigma&quot;: [ # Noise sigma per feature. No noise is added to features that are not set.
              { # Noise sigma for a single feature.
                &quot;name&quot;: &quot;A String&quot;, # The name of the input feature for which noise sigma is provided. The features are defined in explanation metadata inputs.
                &quot;sigma&quot;: 3.14, # This represents the standard deviation of the Gaussian kernel that will be used to add noise to the feature prior to computing gradients. Similar to noise_sigma but represents the noise added to the current feature. Defaults to 0.1.
              },
            ],
          },
          &quot;noiseSigma&quot;: 3.14, # This is a single float value and will be used to add noise to all the features. Use this field when all features are normalized to have the same distribution: scale to range [0, 1], [-1, 1] or z-scoring, where features are normalized to have 0-mean and 1-variance. Learn more about [normalization](https://developers.google.com/machine-learning/data-prep/transform/normalization). For best results the recommended value is about 10% - 20% of the standard deviation of the input feature. Refer to section 3.2 of the SmoothGrad paper: https://arxiv.org/pdf/1706.03825.pdf. Defaults to 0.1. If the distribution is different per feature, set feature_noise_sigma instead for each feature.
          &quot;noisySampleCount&quot;: 42, # The number of gradient samples to use for approximation. The higher this number, the more accurate the gradient is, but the runtime complexity increases by this factor as well. Valid range of its value is [1, 50]. Defaults to 3.
        },
        &quot;stepCount&quot;: 42, # Required. The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is met within the desired error range. Valid range of its value is [1, 100], inclusively.
      },
    },
  },
  &quot;modelMonitoringSchema&quot;: { # The Model Monitoring Schema definition. # Monitoring Schema is to specify the model&#x27;s features, prediction outputs and ground truth properties. It is used to extract pertinent data from the dataset and to process features based on their properties. Make sure that the schema aligns with your dataset, if it does not, we will be unable to extract data from the dataset. It is required for most models, but optional for Vertex AI AutoML Tables unless the schem information is not available.
    &quot;featureFields&quot;: [ # Feature names of the model. Vertex AI will try to match the features from your dataset as follows: * For &#x27;csv&#x27; files, the header names are required, and we will extract the corresponding feature values when the header names align with the feature names. * For &#x27;jsonl&#x27; files, we will extract the corresponding feature values if the key names match the feature names. Note: Nested features are not supported, so please ensure your features are flattened. Ensure the feature values are scalar or an array of scalars. * For &#x27;bigquery&#x27; dataset, we will extract the corresponding feature values if the column names match the feature names. Note: The column type can be a scalar or an array of scalars. STRUCT or JSON types are not supported. You may use SQL queries to select or aggregate the relevant features from your original table. However, ensure that the &#x27;schema&#x27; of the query results meets our requirements. * For the Vertex AI Endpoint Request Response Logging table or Vertex AI Batch Prediction Job results. If the instance_type is an array, ensure that the sequence in feature_fields matches the order of features in the prediction instance. We will match the feature with the array in the order specified in [feature_fields].
      { # Schema field definition.
        &quot;dataType&quot;: &quot;A String&quot;, # Supported data types are: `float` `integer` `boolean` `string` `categorical`
        &quot;name&quot;: &quot;A String&quot;, # Field name.
        &quot;repeated&quot;: True or False, # Describes if the schema field is an array of given data type.
      },
    ],
    &quot;groundTruthFields&quot;: [ # Target /ground truth names of the model.
      { # Schema field definition.
        &quot;dataType&quot;: &quot;A String&quot;, # Supported data types are: `float` `integer` `boolean` `string` `categorical`
        &quot;name&quot;: &quot;A String&quot;, # Field name.
        &quot;repeated&quot;: True or False, # Describes if the schema field is an array of given data type.
      },
    ],
    &quot;predictionFields&quot;: [ # Prediction output names of the model. The requirements are the same as the feature_fields. For AutoML Tables, the prediction output name presented in schema will be: `predicted_{target_column}`, the `target_column` is the one you specified when you train the model. For Prediction output drift analysis: * AutoML Classification, the distribution of the argmax label will be analyzed. * AutoML Regression, the distribution of the value will be analyzed.
      { # Schema field definition.
        &quot;dataType&quot;: &quot;A String&quot;, # Supported data types are: `float` `integer` `boolean` `string` `categorical`
        &quot;name&quot;: &quot;A String&quot;, # Field name.
        &quot;repeated&quot;: True or False, # Describes if the schema field is an array of given data type.
      },
    ],
  },
  &quot;modelMonitoringTarget&quot;: { # The monitoring target refers to the entity that is subject to analysis. e.g. Vertex AI Model version. # The entity that is subject to analysis. Currently only models in Vertex AI Model Registry are supported. If you want to analyze the model which is outside the Vertex AI, you could register a model in Vertex AI Model Registry using just a display name.
    &quot;vertexModel&quot;: { # Model in Vertex AI Model Registry. # Model in Vertex AI Model Registry.
      &quot;model&quot;: &quot;A String&quot;, # Model resource name. Format: projects/{project}/locations/{location}/models/{model}.
      &quot;modelVersionId&quot;: &quot;A String&quot;, # Model version id.
    },
  },
  &quot;name&quot;: &quot;A String&quot;, # Immutable. Resource name of the ModelMonitor. Format: `projects/{project}/locations/{location}/modelMonitors/{model_monitor}`.
  &quot;notificationSpec&quot;: { # Notification spec(email, notification channel) for model monitoring statistics/alerts. # Optional default notification spec, it can be overridden in the ModelMonitoringJob notification spec.
    &quot;emailConfig&quot;: { # The config for email alerts. # Email alert config.
      &quot;userEmails&quot;: [ # The email addresses to send the alerts.
        &quot;A String&quot;,
      ],
    },
    &quot;enableCloudLogging&quot;: True or False, # Dump the anomalies to Cloud Logging. The anomalies will be put to json payload encoded from proto google.cloud.aiplatform.logging.ModelMonitoringAnomaliesLogEntry. This can be further sinked to Pub/Sub or any other services supported by Cloud Logging.
    &quot;notificationChannelConfigs&quot;: [ # Notification channel config.
      { # Google Cloud Notification Channel config.
        &quot;notificationChannel&quot;: &quot;A String&quot;, # Resource names of the NotificationChannels. Must be of the format `projects//notificationChannels/`
      },
    ],
  },
  &quot;outputSpec&quot;: { # Specification for the export destination of monitoring results, including metrics, logs, etc. # Optional default monitoring metrics/logs export spec, it can be overridden in the ModelMonitoringJob output spec. If not specified, a default Google Cloud Storage bucket will be created under your project.
    &quot;gcsBaseDirectory&quot;: { # The Google Cloud Storage location where the output is to be written to. # Google Cloud Storage base folder path for metrics, error logs, etc.
      &quot;outputUriPrefix&quot;: &quot;A String&quot;, # Required. Google Cloud Storage URI to output directory. If the uri doesn&#x27;t end with &#x27;/&#x27;, a &#x27;/&#x27; will be automatically appended. The directory is created if it doesn&#x27;t exist.
    },
  },
  &quot;satisfiesPzi&quot;: True or False, # Output only. Reserved for future use.
  &quot;satisfiesPzs&quot;: True or False, # Output only. Reserved for future use.
  &quot;tabularObjective&quot;: { # Tabular monitoring objective. # Optional default tabular model monitoring objective.
    &quot;featureAttributionSpec&quot;: { # Feature attribution monitoring spec. # Feature attribution monitoring spec.
      &quot;batchExplanationDedicatedResources&quot;: { # A description of resources that are used for performing batch operations, are dedicated to a Model, and need manual configuration. # The config of resources used by the Model Monitoring during the batch explanation for non-AutoML models. If not set, `n1-standard-2` machine type will be used by default.
        &quot;flexStart&quot;: { # FlexStart is used to schedule the deployment workload on DWS resource. It contains the max duration of the deployment. # Optional. Immutable. If set, use DWS resource to schedule the deployment workload. reference: (https://cloud.google.com/blog/products/compute/introducing-dynamic-workload-scheduler)
          &quot;maxRuntimeDuration&quot;: &quot;A String&quot;, # The max duration of the deployment is max_runtime_duration. The deployment will be terminated after the duration. The max_runtime_duration can be set up to 7 days.
        },
        &quot;machineSpec&quot;: { # Specification of a single machine. # Required. Immutable. The specification of a single machine.
          &quot;acceleratorCount&quot;: 42, # The number of accelerators to attach to the machine.
          &quot;acceleratorType&quot;: &quot;A String&quot;, # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
          &quot;gpuPartitionSize&quot;: &quot;A String&quot;, # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size=&quot;1g.10gb&quot;, the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1.
          &quot;machineType&quot;: &quot;A String&quot;, # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
          &quot;multihostGpuNodeCount&quot;: 42, # Optional. Immutable. The number of nodes per replica for multihost GPU deployments.
          &quot;reservationAffinity&quot;: { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
            &quot;key&quot;: &quot;A String&quot;, # Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use `compute.googleapis.com/reservation-name` as the key and specify the name of your reservation as its value.
            &quot;reservationAffinityType&quot;: &quot;A String&quot;, # Required. Specifies the reservation affinity type.
            &quot;values&quot;: [ # Optional. Corresponds to the label values of a reservation resource. This must be the full resource name of the reservation or reservation block.
              &quot;A String&quot;,
            ],
          },
          &quot;tpuTopology&quot;: &quot;A String&quot;, # Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: &quot;2x2x1&quot;).
        },
        &quot;maxReplicaCount&quot;: 42, # Immutable. The maximum number of machine replicas the batch operation may be scaled to. The default value is 10.
        &quot;spot&quot;: True or False, # Optional. If true, schedule the deployment workload on [spot VMs](https://cloud.google.com/kubernetes-engine/docs/concepts/spot-vms).
        &quot;startingReplicaCount&quot;: 42, # Immutable. The number of machine replicas used at the start of the batch operation. If not set, Vertex AI decides starting number, not greater than max_replica_count
      },
      &quot;defaultAlertCondition&quot;: { # Monitoring alert triggered condition. # Default alert condition for all the features.
        &quot;threshold&quot;: 3.14, # A condition that compares a stats value against a threshold. Alert will be triggered if value above the threshold.
      },
      &quot;featureAlertConditions&quot;: { # Per feature alert condition will override default alert condition.
        &quot;a_key&quot;: { # Monitoring alert triggered condition.
          &quot;threshold&quot;: 3.14, # A condition that compares a stats value against a threshold. Alert will be triggered if value above the threshold.
        },
      },
      &quot;features&quot;: [ # Feature names interested in monitoring. These should be a subset of the input feature names specified in the monitoring schema. If the field is not specified all features outlied in the monitoring schema will be used.
        &quot;A String&quot;,
      ],
    },
    &quot;featureDriftSpec&quot;: { # Data drift monitoring spec. Data drift measures the distribution distance between the current dataset and a baseline dataset. A typical use case is to detect data drift between the recent production serving dataset and the training dataset, or to compare the recent production dataset with a dataset from a previous period. # Input feature distribution drift monitoring spec.
      &quot;categoricalMetricType&quot;: &quot;A String&quot;, # Supported metrics type: * l_infinity * jensen_shannon_divergence
      &quot;defaultCategoricalAlertCondition&quot;: { # Monitoring alert triggered condition. # Default alert condition for all the categorical features.
        &quot;threshold&quot;: 3.14, # A condition that compares a stats value against a threshold. Alert will be triggered if value above the threshold.
      },
      &quot;defaultNumericAlertCondition&quot;: { # Monitoring alert triggered condition. # Default alert condition for all the numeric features.
        &quot;threshold&quot;: 3.14, # A condition that compares a stats value against a threshold. Alert will be triggered if value above the threshold.
      },
      &quot;featureAlertConditions&quot;: { # Per feature alert condition will override default alert condition.
        &quot;a_key&quot;: { # Monitoring alert triggered condition.
          &quot;threshold&quot;: 3.14, # A condition that compares a stats value against a threshold. Alert will be triggered if value above the threshold.
        },
      },
      &quot;features&quot;: [ # Feature names / Prediction output names interested in monitoring. These should be a subset of the input feature names or prediction output names specified in the monitoring schema. If the field is not specified all features / prediction outputs outlied in the monitoring schema will be used.
        &quot;A String&quot;,
      ],
      &quot;numericMetricType&quot;: &quot;A String&quot;, # Supported metrics type: * jensen_shannon_divergence
    },
    &quot;predictionOutputDriftSpec&quot;: { # Data drift monitoring spec. Data drift measures the distribution distance between the current dataset and a baseline dataset. A typical use case is to detect data drift between the recent production serving dataset and the training dataset, or to compare the recent production dataset with a dataset from a previous period. # Prediction output distribution drift monitoring spec.
      &quot;categoricalMetricType&quot;: &quot;A String&quot;, # Supported metrics type: * l_infinity * jensen_shannon_divergence
      &quot;defaultCategoricalAlertCondition&quot;: { # Monitoring alert triggered condition. # Default alert condition for all the categorical features.
        &quot;threshold&quot;: 3.14, # A condition that compares a stats value against a threshold. Alert will be triggered if value above the threshold.
      },
      &quot;defaultNumericAlertCondition&quot;: { # Monitoring alert triggered condition. # Default alert condition for all the numeric features.
        &quot;threshold&quot;: 3.14, # A condition that compares a stats value against a threshold. Alert will be triggered if value above the threshold.
      },
      &quot;featureAlertConditions&quot;: { # Per feature alert condition will override default alert condition.
        &quot;a_key&quot;: { # Monitoring alert triggered condition.
          &quot;threshold&quot;: 3.14, # A condition that compares a stats value against a threshold. Alert will be triggered if value above the threshold.
        },
      },
      &quot;features&quot;: [ # Feature names / Prediction output names interested in monitoring. These should be a subset of the input feature names or prediction output names specified in the monitoring schema. If the field is not specified all features / prediction outputs outlied in the monitoring schema will be used.
        &quot;A String&quot;,
      ],
      &quot;numericMetricType&quot;: &quot;A String&quot;, # Supported metrics type: * jensen_shannon_divergence
    },
  },
  &quot;trainingDataset&quot;: { # Model monitoring data input spec. # Optional training dataset used to train the model. It can serve as a reference dataset to identify changes in production.
    &quot;batchPredictionOutput&quot;: { # Data from Vertex AI Batch prediction job output. # Vertex AI Batch prediction Job.
      &quot;batchPredictionJob&quot;: &quot;A String&quot;, # Vertex AI Batch prediction job resource name. The job must match the model version specified in [ModelMonitor].[model_monitoring_target].
    },
    &quot;columnizedDataset&quot;: { # Input dataset spec. # Columnized dataset.
      &quot;bigquerySource&quot;: { # Dataset spec for data sotred in BigQuery. # BigQuery data source.
        &quot;query&quot;: &quot;A String&quot;, # Standard SQL to be used instead of the `table_uri`.
        &quot;tableUri&quot;: &quot;A String&quot;, # BigQuery URI to a table, up to 2000 characters long. All the columns in the table will be selected. Accepted forms: * BigQuery path. For example: `bq://projectId.bqDatasetId.bqTableId`.
      },
      &quot;gcsSource&quot;: { # Dataset spec for data stored in Google Cloud Storage. # Google Cloud Storage data source.
        &quot;format&quot;: &quot;A String&quot;, # Data format of the dataset.
        &quot;gcsUri&quot;: &quot;A String&quot;, # Google Cloud Storage URI to the input file(s). May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/wildcards.
      },
      &quot;timestampField&quot;: &quot;A String&quot;, # The timestamp field. Usually for serving data.
      &quot;vertexDataset&quot;: &quot;A String&quot;, # Resource name of the Vertex AI managed dataset.
    },
    &quot;timeInterval&quot;: { # Represents a time interval, encoded as a Timestamp start (inclusive) and a Timestamp end (exclusive). The start must be less than or equal to the end. When the start equals the end, the interval is empty (matches no time). When both start and end are unspecified, the interval matches any time. # The time interval (pair of start_time and end_time) for which results should be returned.
      &quot;endTime&quot;: &quot;A String&quot;, # Optional. Exclusive end of the interval. If specified, a Timestamp matching this interval will have to be before the end.
      &quot;startTime&quot;: &quot;A String&quot;, # Optional. Inclusive start of the interval. If specified, a Timestamp matching this interval will have to be the same or after the start.
    },
    &quot;timeOffset&quot;: { # Time offset setting. # The time offset setting for which results should be returned.
      &quot;offset&quot;: &quot;A String&quot;, # [offset] is the time difference from the cut-off time. For scheduled jobs, the cut-off time is the scheduled time. For non-scheduled jobs, it&#x27;s the time when the job was created. Currently we support the following format: &#x27;w|W&#x27;: Week, &#x27;d|D&#x27;: Day, &#x27;h|H&#x27;: Hour E.g. &#x27;1h&#x27; stands for 1 hour, &#x27;2d&#x27; stands for 2 days.
      &quot;window&quot;: &quot;A String&quot;, # [window] refers to the scope of data selected for analysis. It allows you to specify the quantity of data you wish to examine. Currently we support the following format: &#x27;w|W&#x27;: Week, &#x27;d|D&#x27;: Day, &#x27;h|H&#x27;: Hour E.g. &#x27;1h&#x27; stands for 1 hour, &#x27;2d&#x27; stands for 2 days.
    },
    &quot;vertexEndpointLogs&quot;: { # Data from Vertex AI Endpoint request response logging. # Vertex AI Endpoint request &amp; response logging.
      &quot;endpoints&quot;: [ # List of endpoint resource names. The endpoints must enable the logging with the [Endpoint].[request_response_logging_config], and must contain the deployed model corresponding to the model version specified in [ModelMonitor].[model_monitoring_target].
        &quot;A String&quot;,
      ],
    },
  },
  &quot;updateTime&quot;: &quot;A String&quot;, # Output only. Timestamp when this ModelMonitor was updated most recently.
}

  updateMask: string, Required. Mask specifying which fields to update.
  x__xgafv: string, V1 error format.
    Allowed values
      1 - v1 error format
      2 - v2 error format

Returns:
  An object of the form:

    { # This resource represents a long-running operation that is the result of a network API call.
  &quot;done&quot;: True or False, # If the value is `false`, it means the operation is still in progress. If `true`, the operation is completed, and either `error` or `response` is available.
  &quot;error&quot;: { # The `Status` type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by [gRPC](https://github.com/grpc). Each `Status` message contains three pieces of data: error code, error message, and error details. You can find out more about this error model and how to work with it in the [API Design Guide](https://cloud.google.com/apis/design/errors). # The error result of the operation in case of failure or cancellation.
    &quot;code&quot;: 42, # The status code, which should be an enum value of google.rpc.Code.
    &quot;details&quot;: [ # A list of messages that carry the error details. There is a common set of message types for APIs to use.
      {
        &quot;a_key&quot;: &quot;&quot;, # Properties of the object. Contains field @type with type URL.
      },
    ],
    &quot;message&quot;: &quot;A String&quot;, # A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.
  },
  &quot;metadata&quot;: { # Service-specific metadata associated with the operation. It typically contains progress information and common metadata such as create time. Some services might not provide such metadata. Any method that returns a long-running operation should document the metadata type, if any.
    &quot;a_key&quot;: &quot;&quot;, # Properties of the object. Contains field @type with type URL.
  },
  &quot;name&quot;: &quot;A String&quot;, # The server-assigned name, which is only unique within the same service that originally returns it. If you use the default HTTP mapping, the `name` should be a resource name ending with `operations/{unique_id}`.
  &quot;response&quot;: { # The normal, successful response of the operation. If the original method returns no data on success, such as `Delete`, the response is `google.protobuf.Empty`. If the original method is standard `Get`/`Create`/`Update`, the response should be the resource. For other methods, the response should have the type `XxxResponse`, where `Xxx` is the original method name. For example, if the original method name is `TakeSnapshot()`, the inferred response type is `TakeSnapshotResponse`.
    &quot;a_key&quot;: &quot;&quot;, # Properties of the object. Contains field @type with type URL.
  },
}</pre>
</div>

<div class="method">
    <code class="details" id="searchModelMonitoringAlerts">searchModelMonitoringAlerts(modelMonitor, body=None, x__xgafv=None)</code>
  <pre>Returns the Model Monitoring alerts.

Args:
  modelMonitor: string, Required. ModelMonitor resource name. Format: `projects/{project}/locations/{location}/modelMonitors/{model_monitor}` (required)
  body: object, The request body.
    The object takes the form of:

{ # Request message for ModelMonitoringService.SearchModelMonitoringAlerts.
  &quot;alertTimeInterval&quot;: { # Represents a time interval, encoded as a Timestamp start (inclusive) and a Timestamp end (exclusive). The start must be less than or equal to the end. When the start equals the end, the interval is empty (matches no time). When both start and end are unspecified, the interval matches any time. # If non-empty, returns the alerts in this time interval.
    &quot;endTime&quot;: &quot;A String&quot;, # Optional. Exclusive end of the interval. If specified, a Timestamp matching this interval will have to be before the end.
    &quot;startTime&quot;: &quot;A String&quot;, # Optional. Inclusive start of the interval. If specified, a Timestamp matching this interval will have to be the same or after the start.
  },
  &quot;modelMonitoringJob&quot;: &quot;A String&quot;, # If non-empty, returns the alerts of this model monitoring job.
  &quot;objectiveType&quot;: &quot;A String&quot;, # If non-empty, returns the alerts of this objective type. Supported monitoring objectives: `raw-feature-drift` `prediction-output-drift` `feature-attribution`
  &quot;pageSize&quot;: 42, # The standard list page size.
  &quot;pageToken&quot;: &quot;A String&quot;, # A page token received from a previous ModelMonitoringService.SearchModelMonitoringAlerts call.
  &quot;statsName&quot;: &quot;A String&quot;, # If non-empty, returns the alerts of this stats_name.
}

  x__xgafv: string, V1 error format.
    Allowed values
      1 - v1 error format
      2 - v2 error format

Returns:
  An object of the form:

    { # Response message for ModelMonitoringService.SearchModelMonitoringAlerts.
  &quot;modelMonitoringAlerts&quot;: [ # Alerts retrieved for the requested objectives. Sorted by alert time descendingly.
    { # Represents a single monitoring alert. This is currently used in the SearchModelMonitoringAlerts api, thus the alert wrapped in this message belongs to the resource asked in the request.
      &quot;alertTime&quot;: &quot;A String&quot;, # Alert creation time.
      &quot;anomaly&quot;: { # Represents a single model monitoring anomaly. # Anomaly details.
        &quot;algorithm&quot;: &quot;A String&quot;, # Algorithm used to calculated the metrics, eg: jensen_shannon_divergence, l_infinity.
        &quot;modelMonitoringJob&quot;: &quot;A String&quot;, # Model monitoring job resource name.
        &quot;tabularAnomaly&quot;: { # Tabular anomaly details. # Tabular anomaly.
          &quot;anomaly&quot;: &quot;&quot;, # Anomaly body.
          &quot;anomalyUri&quot;: &quot;A String&quot;, # Additional anomaly information. e.g. Google Cloud Storage uri.
          &quot;condition&quot;: { # Monitoring alert triggered condition. # The alert condition associated with this anomaly.
            &quot;threshold&quot;: 3.14, # A condition that compares a stats value against a threshold. Alert will be triggered if value above the threshold.
          },
          &quot;summary&quot;: &quot;A String&quot;, # Overview of this anomaly.
          &quot;triggerTime&quot;: &quot;A String&quot;, # The time the anomaly was triggered.
        },
      },
      &quot;objectiveType&quot;: &quot;A String&quot;, # One of the supported monitoring objectives: `raw-feature-drift` `prediction-output-drift` `feature-attribution`
      &quot;statsName&quot;: &quot;A String&quot;, # The stats name.
    },
  ],
  &quot;nextPageToken&quot;: &quot;A String&quot;, # The page token that can be used by the next ModelMonitoringService.SearchModelMonitoringAlerts call.
  &quot;totalNumberAlerts&quot;: &quot;A String&quot;, # The total number of alerts retrieved by the requested objectives.
}</pre>
</div>

<div class="method">
    <code class="details" id="searchModelMonitoringAlerts_next">searchModelMonitoringAlerts_next()</code>
  <pre>Retrieves the next page of results.

        Args:
          previous_request: The request for the previous page. (required)
          previous_response: The response from the request for the previous page. (required)

        Returns:
          A request object that you can call &#x27;execute()&#x27; on to request the next
          page. Returns None if there are no more items in the collection.
        </pre>
</div>

<div class="method">
    <code class="details" id="searchModelMonitoringStats">searchModelMonitoringStats(modelMonitor, body=None, x__xgafv=None)</code>
  <pre>Searches Model Monitoring Stats generated within a given time window.

Args:
  modelMonitor: string, Required. ModelMonitor resource name. Format: `projects/{project}/locations/{location}/modelMonitors/{model_monitor}` (required)
  body: object, The request body.
    The object takes the form of:

{ # Request message for ModelMonitoringService.SearchModelMonitoringStats.
  &quot;pageSize&quot;: 42, # The standard list page size.
  &quot;pageToken&quot;: &quot;A String&quot;, # A page token received from a previous ModelMonitoringService.SearchModelMonitoringStats call.
  &quot;statsFilter&quot;: { # Filter for searching ModelMonitoringStats. # Filter for search different stats.
    &quot;tabularStatsFilter&quot;: { # Tabular statistics filter. # Tabular statistics filter.
      &quot;algorithm&quot;: &quot;A String&quot;, # Specify the algorithm type used for distance calculation, eg: jensen_shannon_divergence, l_infinity.
      &quot;modelMonitoringJob&quot;: &quot;A String&quot;, # From a particular monitoring job.
      &quot;modelMonitoringSchedule&quot;: &quot;A String&quot;, # From a particular monitoring schedule.
      &quot;objectiveType&quot;: &quot;A String&quot;, # One of the supported monitoring objectives: `raw-feature-drift` `prediction-output-drift` `feature-attribution`
      &quot;statsName&quot;: &quot;A String&quot;, # If not specified, will return all the stats_names.
    },
  },
  &quot;timeInterval&quot;: { # Represents a time interval, encoded as a Timestamp start (inclusive) and a Timestamp end (exclusive). The start must be less than or equal to the end. When the start equals the end, the interval is empty (matches no time). When both start and end are unspecified, the interval matches any time. # The time interval for which results should be returned.
    &quot;endTime&quot;: &quot;A String&quot;, # Optional. Exclusive end of the interval. If specified, a Timestamp matching this interval will have to be before the end.
    &quot;startTime&quot;: &quot;A String&quot;, # Optional. Inclusive start of the interval. If specified, a Timestamp matching this interval will have to be the same or after the start.
  },
}

  x__xgafv: string, V1 error format.
    Allowed values
      1 - v1 error format
      2 - v2 error format

Returns:
  An object of the form:

    { # Response message for ModelMonitoringService.SearchModelMonitoringStats.
  &quot;monitoringStats&quot;: [ # Stats retrieved for requested objectives.
    { # Represents the collection of statistics for a metric.
      &quot;tabularStats&quot;: { # A collection of data points that describes the time-varying values of a tabular metric. # Generated tabular statistics.
        &quot;dataPoints&quot;: [ # The data points of this time series. When listing time series, points are returned in reverse time order.
          { # Represents a single statistics data point.
            &quot;algorithm&quot;: &quot;A String&quot;, # Algorithm used to calculated the metrics, eg: jensen_shannon_divergence, l_infinity.
            &quot;baselineStats&quot;: { # Typed value of the statistics. # Statistics from baseline dataset.
              &quot;distributionValue&quot;: { # Summary statistics for a population of values. # Distribution.
                &quot;distribution&quot;: &quot;&quot;, # Predictive monitoring drift distribution in `tensorflow.metadata.v0.DatasetFeatureStatistics` format.
                &quot;distributionDeviation&quot;: 3.14, # Distribution distance deviation from the current dataset&#x27;s statistics to baseline dataset&#x27;s statistics. * For categorical feature, the distribution distance is calculated by L-inifinity norm or Jensen–Shannon divergence. * For numerical feature, the distribution distance is calculated by Jensen–Shannon divergence.
              },
              &quot;doubleValue&quot;: 3.14, # Double.
            },
            &quot;createTime&quot;: &quot;A String&quot;, # Statistics create time.
            &quot;currentStats&quot;: { # Typed value of the statistics. # Statistics from current dataset.
              &quot;distributionValue&quot;: { # Summary statistics for a population of values. # Distribution.
                &quot;distribution&quot;: &quot;&quot;, # Predictive monitoring drift distribution in `tensorflow.metadata.v0.DatasetFeatureStatistics` format.
                &quot;distributionDeviation&quot;: 3.14, # Distribution distance deviation from the current dataset&#x27;s statistics to baseline dataset&#x27;s statistics. * For categorical feature, the distribution distance is calculated by L-inifinity norm or Jensen–Shannon divergence. * For numerical feature, the distribution distance is calculated by Jensen–Shannon divergence.
              },
              &quot;doubleValue&quot;: 3.14, # Double.
            },
            &quot;hasAnomaly&quot;: True or False, # Indicate if the statistics has anomaly.
            &quot;modelMonitoringJob&quot;: &quot;A String&quot;, # Model monitoring job resource name.
            &quot;schedule&quot;: &quot;A String&quot;, # Schedule resource name.
            &quot;thresholdValue&quot;: 3.14, # Threshold value.
          },
        ],
        &quot;objectiveType&quot;: &quot;A String&quot;, # One of the supported monitoring objectives: `raw-feature-drift` `prediction-output-drift` `feature-attribution`
        &quot;statsName&quot;: &quot;A String&quot;, # The stats name.
      },
    },
  ],
  &quot;nextPageToken&quot;: &quot;A String&quot;, # The page token that can be used by the next ModelMonitoringService.SearchModelMonitoringStats call.
}</pre>
</div>

<div class="method">
    <code class="details" id="searchModelMonitoringStats_next">searchModelMonitoringStats_next()</code>
  <pre>Retrieves the next page of results.

        Args:
          previous_request: The request for the previous page. (required)
          previous_response: The response from the request for the previous page. (required)

        Returns:
          A request object that you can call &#x27;execute()&#x27; on to request the next
          page. Returns None if there are no more items in the collection.
        </pre>
</div>

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